Collecting user information from computer systems

1. A computer-implemented method, comprising:

acquiring and identifying, by an acquisition unit of a computing device and as acquired and identified textual information, textual information related to user information from user service information provided by an on-line user-applicable service;

performing, by a matching unit of the computing device, a layered matching on the acquired and identified textual information based on preset background identification information in a preset list, wherein the layered matching includes a sequence of different matching methods based upon a descending order of matching accuracies, and the preset list includes a plurality of entries storing different preset background identification information related to the user information, the layered matching comprising:

performing a first matching method based upon a first match accuracy between a first subset of the acquired and identified textual information and the plurality of entries of the preset list to produce a saved first matching method result;

in response to the saved first match method result from the first matching method, determining a lack of exact match between the first subset of the acquired and identified textual information and the plurality of entries of the preset list;

in response to determining the lack of exact match, performing a second matching method based upon a second matching accuracy between the first subset of the acquired and identified textual information and the plurality of entries of the preset list to produce a saved second matching method result;

in response to the saved second matching method result from the second matching method, determining a unique match between the first subset of the acquired and identified textual information and a unique one of the plurality of entries of the preset list, the unique match indicating that the first subset of the acquired and identified textual information contains or is contained in the unique one of the plurality of entries of the preset list;

in response to determining the unique match, updating the preset list by an updating unit of the computing device, to provide an association between the first subset of the acquired and identified textual information and the unique one of the plurality of entries of the preset list; and

performing a third matching method based upon a preset similarity formula having a preset similarity threshold between the acquired and identified textual information and the plurality of entries of the preset list to produce a similarity match result, wherein the preset similarity formula comprises:

determining a type of language of the textual information;

calculating, based upon the type of language of the textual information, a similarity value between the acquired and identified textual information and each of the plurality of entries in the preset list using the preset similarity formula based on a largest common sub-sequence algorithm;

determining a largest similarity value; and

in response to the largest similarity value being greater than or equal to the preset similarity threshold, setting an entry of the plurality of entries in the preset list corresponding to the largest similarity value as the similarity match result;

determining, by a collection unit of the computing device, the user information based on the saved first matching method result, the saved second matching method result, and the similarity match result of the layered matching; and

collecting, by the collection unit of the computing device, the determined user information.

2. The computer-implemented method of claim 1 , wherein calculating the similarity value between the acquired and identified textual information and each of the plurality of entries in the preset list using the preset similarity formula having the preset similarity threshold comprises:

calculating a longest common sub-string length between the acquired and identified textual information and that particular entry of the plurality of entries in the preset list; and

dividing the longest common sub-string length by a string length of that particular entry of the plurality of entries.

3. The computer-implemented method of claim 1 , further comprising:

performing, by a cleaning unit of the computing device and as cleaned textual information, data cleaning on the acquired and identified textual information; and

performing the layered matching on the cleaned textual information.

4. The computer-implemented method of claim 1 , wherein the textual information and the user service information have a same subject configuration.

5. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

acquiring and identifying, by an acquisition unit of a computing device and as acquired and identified textual information, textual information related to user information from user service information provided by an on-line user-applicable service;

performing, by a matching unit of the computing device, a layered matching on the acquired and identified textual information based on preset background identification information in a preset list, wherein the layered matching includes a sequence of different matching methods based upon a descending order of matching accuracies, and the preset list includes a plurality of entries storing different preset background identification information related to the user information, the layered matching comprising:

performing a first matching method based upon a first match accuracy between a first subset of the acquired and identified textual information and the plurality of entries of the preset list to produce a saved first matching method result;

in response to the saved first match method result from the first matching method, determining a lack of exact match between the first subset of the acquired and identified textual information and the plurality of entries of the preset list;

in response to determining the lack of exact match, performing a second matching method based upon a second matching accuracy between the first subset of the acquired and identified textual information and the plurality of entries of the preset list to produce a saved second matching method result;

in response to the saved second matching method result from the second matching method, determining a unique match between the first subset of the acquired and identified textual information and a unique one of the plurality of entries of the preset list, the unique match indicating that the first subset of the acquired and identified textual information contains or is contained in the unique one of the plurality of entries of the preset list;

in response to determining the unique match, updating the preset list by an updating unit of the computing device, to provide an association between the first subset of the acquired and identified textual information and the unique one of the plurality of entries of the preset list; and

performing a third matching method based upon a preset similarity formula having a preset similarity threshold between the acquired and identified textual information and the plurality of entries of the preset list to produce a similarity match result, wherein the preset similarity formula comprises:

determining a type of language of the textual information;

calculating, based upon the type of language of the textual information, a similarity value between the acquired and identified textual information and each of the plurality of entries in the preset list using the preset similarity formula based on a largest common sub-sequence algorithm;

determining a largest similarity value;

in response to the largest similarity value being greater than or equal to the preset similarity threshold, setting an entry of the plurality of entries in the preset list corresponding to the largest similarity value as the similarity match result;

determining, by a collection unit of the computing device, the user information based on the saved first matching method result, the saved second matching method result, and the similarity match result of the layered matching; and

collecting, by the collection unit of the computing device, the determined user information.

6. The non-transitory, computer-readable medium of claim 5 , wherein calculating the similarity value between the acquired and identified textual information and each of the plurality of entries in the preset list using the preset similarity formula having the preset similarity threshold comprises one or more instructions to:

calculating a longest common sub-string length between the acquired and identified textual information and that particular entry of the plurality of entries in the preset list; and

dividing the longest common sub-string length by a string length of that particular entry of the plurality of entries.

7. The non-transitory, computer-readable medium of claim 5 , further comprising one or more instructions to:

performing, by a cleaning unit of the computing device and as cleaned textual information, data cleaning on the acquired and identified textual information; and

performing the layered matching on the cleaned textual information.

8. The non-transitory, computer-readable medium of claim 5 , wherein the textual information and the user service information have a same subject configuration.

9. A computer-implemented system, comprising:

a computer memory; and

a hardware processor interoperably coupled with the computer memory and configured to perform operations comprising:

acquiring and identifying, by an acquisition unit of a computing device and as acquired and identified textual information, textual information related to user information from user service information provided by an on-line user-applicable service;

performing, by a matching unit of the computing device, a layered matching on the acquired and identified textual information based on preset background identification information in a preset list, wherein the layered matching includes a sequence of different matching methods based upon a descending order of matching accuracies, and the preset list includes a plurality of entries storing different preset background identification information related to the user information, the layered matching comprising:

performing a first matching method based upon a first match accuracy between a first subset of the acquired and identified textual information and the plurality of entries of the preset list to produce a saved first matching method result;

in response to the saved first match method result from the first matching method, determining a lack of exact match between the first subset of the acquired and identified textual information and the plurality of entries of the preset list;

in response to determining the lack of exact match, performing a second matching method based upon a second matching accuracy between the first subset of the acquired and identified textual information and the plurality of entries of the preset list to produce a saved second matching method result;

in response to the saved second matching method result from the second matching method, determining a unique match between the first subset of the acquired and identified textual information and a unique one of the plurality of entries of the preset list, the unique match indicating that the first subset of the acquired and identified textual information contains or is contained in the unique one of the plurality of entries of the preset list;

in response to determining the unique match, updating the preset list by an updating unit of the computing device, to provide an association between the first subset of the acquired and identified textual information and the unique one of the plurality of entries of the preset list; and

performing a third matching method based upon a preset similarity formula having a preset similarity threshold between the acquired and identified textual information and the plurality of entries of the preset list to produce a similarity match result, wherein the preset similarity formula comprises:

determining a type of language of the textual information;

calculating, based upon the type of language of the textual information, a similarity value between the acquired and identified textual information and each of the plurality of entries in the preset list using the preset similarity formula based on a largest common sub-sequence algorithm;

determining a largest similarity value; and

in response to the largest similarity value being greater than or equal to the preset similarity threshold, setting an entry of the plurality of entries in the preset list corresponding to the largest similarity value as the similarity match result;

determining, by a collection unit of the computing device, the user information based on the saved first matching method result, the saved second matching method result, and the similarity match result of the layered matching; and

collecting, by the collection unit of the computing device, the determined user information.

10. The computer-implemented system of claim 9 , wherein calculating the similarity value between the acquired and identified textual information and each of the plurality of entries in the preset list using the preset similarity formula having the preset similarity threshold comprises:

calculating a longest common sub-string length between the acquired and identified textual information and that particular entry of the plurality of entries in the preset list; and

dividing the longest common sub-string length by a string length of that particular entry of the plurality of entries.

11. The computer-implemented system of claim 9 , further configured to:

performing, by a cleaning unit of the computing device and as cleaned textual information, data cleaning on the acquired and identified textual information; and

performing the layered matching on the cleaned textual information.

12. The computer-implemented system of claim 9 , wherein the textual information and the user service information have a same subject configuration.