Application analysis of quality evaluation model based on multi-source data mining

Abstract

In order to improve the accuracy and efficiency of talent training quality evaluation, a design method of quality evaluation model based on multi-source data mining is proposed. The data processing technology is used to preprocess the talent training data, the multi-source data mining method is used to classify the information in the evaluation database, and the information in the quality evaluation database is mapped to the evaluation index system. According to the diversity and complexity of knowledge modules required by aviation service talents, the talent evaluation system is divided into knowledge literacy module, interpersonal communication ability module, work status module, foreign language ability module and service literacy module to obtain various evaluation indicators. Set the evaluation standard, and use Likert scale method and standard deviation method to calculate the weight of each evaluation index. Finally, use BP neural network to build the talent training quality evaluation model and obtain the final evaluation result. The experimental results show that the proposed method has obvious advantages in evaluation accuracy and efficiency.