Benefit evaluation of human resource management in agricultural enterprises based on convolutional neural network

Abstract

With the rapid development of economy, the performance appraisal of human resource management in agricultural enterprises has gained more attention. Based on the convolutional neural network theory, this paper constructs the benefit evaluation system of human resource management in agricultural enterprises, determines the weight to obtain the comprehensive benefit result, and then obtains the evaluation level. According to the main factors affecting the human resource benefit of agricultural enterprises, the model designed the input and output indexes of the efficiency evaluation of single well of production capacity construction, and solved the problem of measuring the informatization ability of human resource management in agricultural enterprises. In the simulation process, the convolutional neural network designed the evaluation index of the index system. According to the different contribution and importance degree to the system security, the difference between the evaluation index can be expressed by assigning different weight values. Secondly, for items with similar loads on multiple factors or with low loads on a single factor, it was adopted to obtain a scale consisting of 54 items, including 15 items in interpersonal skills sub-scale, 16 items in learning development sub-scale and 23 items in growth driver sub-scale. The experimental results show that the convolutional neural network training can obtain the reasonable scale value of the test sample management area. The evaluation index of the model prediction effect shows that the absolute relative error, maximum absolute relative error and average absolute relative error are all within 5%, and the equality coefficient is 0.9845, which is greater than 0.9, indicating that the reasonable scale predicted value has a high degree of fitting with the expected value. The prediction results of the model are ideal, which effectively improves the accuracy of benefit evaluation