Evaluation of Talent Training Model Taking into Account the Knowledge Recognition Algorithm of Multiple Constraint Models

Abstract

In the current talent model analysis, the phenomenon occurs such as serious errors, low efficiency, and poor accuracy. Based on this, a talent training model evaluation method is proposed based on multi-constraint model knowledge recognition algorithm. The knowledge recognition algorithm of the multiple constraint model is used to establish a talent training mode evaluation based on the knowledge recognition algorithm of the multiple constraint model, and the mode weight value is used to adjust the connection weights of the knowledge recognition algorithm of the multiple constraint model. The proposed knowledge recognition algorithm of the multiple constraint model is used to analyze the data index weights of the medical professional talent model in detail, to establish the appraisal model of the talent training. The model can effectively complete the evaluation and analysis of the comprehensive ability of medical professional talents. When using the classifier for training, 76% of the data feature dimensions can be selected for dimension reduction, so as to improve the imbalance problem under the training sample. The characteristics of the various constraint models designed in this paper can be tested by using the classifier. Finally, the experimental analysis results show that the application of the knowledge recognition algorithm of the multiple constraint model to the training mode of medical professionals can significantly enhance the accuracy of evaluation. It has certain reference significance in personnel training.