A design method of a big data case evaluation model, a talent evaluation method, a talent pool construction and recommendation method

Abstract

The invention discloses a design method of a big data case evaluation model, a talent evaluation method, and a talent pool construction and recommendation method. Information, and processing information; judge the event processing status, and select all cases whose processing status is completed as historical cases; for historical cases, those with ratings are directly used as satisfaction ratings, and those without ratings are processed through natural language processing NLP algorithm and A multinomial logistic regression model was used to calculate satisfaction scores for historical cases based on corresponding reviews. Through the evaluation model, cases can be effectively scored according to comments or handling opinions, and the quantitative evaluation of cases and talents has been realized.

Description

technical field [0001] The present invention relates to the fields of natural language processing and intelligent case processing, and in particular to a design method of a big data case evaluation model, a talent evaluation method, and a talent pool construction and recommendation method. Background technique [0002] At present, the work of the case department in our country mainly relies on manual processing, which relies heavily on the knowledge reserve and professional level of the staff. How the case processing department sorts and assigns cases depends entirely on the experience of the registered personnel. Staff members are assessed on their individual areas of expertise. In the process of case handling, the applicant will directly score the case, but most of the time, the applicant often does not directly give a score, but only gives a comment, and cannot give a quantitative evaluation of the case handling result; for the case It can automatically match the staff w...

AI Technical Summary

Problems solved by technology

In the process of case handling, the applicant will directly score the case, but most of the time, the applicant often does not directly give a score, but only gives a comment, and cannot give a quantitative evaluation of the case handling result; for the case It can automatically match the staff who are good at handling the incident through the intelligent recommendation algorithm, reduce the burden of case handling from the perspective of the case handling staff and the applicant, improve the efficiency of case handling, and improve the satisfaction of the public on case handling services

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