Construction of College Students’ Employment Quality Evaluation Model System under the Background of Digitalization

Abstract

In the era of knowledge economy, human resources are being valued by various countries and regions. The report of the 19th National Congress of the Communist Party of China pointed out that “talent is a strategic resource for realizing national rejuvenation and winning the initiative of international competition. We must adhere to the principle of the party’s management of talents, gather talents from all over the world and use them, and accelerate the construction of a strong country with talents.” College students are an important part of talents, and their employment intentions directly affect employment behavior. With the development of education in our country, the enrollment quota of most colleges and universities in the country has gradually increased, and the number of graduates has also increased. Social and economic development has different needs for different professional and technical personnel, and the employment situation in different regions is uneven. Under the increasingly complex employment environment, college students have to face greater employment pressure and compete with each other in a narrower employment field. Therefore, it is necessary to conduct better employment guidance and employment quality evaluation for college students. Based on the improved algorithm of BP network, an artificial intelligence-based employment quality evaluation model is constructed. The design model is optimized by introducing a momentum variable factor, adjusting the learning rate and quasi-Newton method, and training and recommending each optimization model through the training data. The experimental results show that the iterations of the gradient descent algorithm and the additional momentum optimization algorithm are far more than 1000 times. Second, the optimal validation errors of the two algorithms are large and the model performance is poor. The quasi-Newton ring algorithm also has faster coordination speed, stronger stability, and better overall performance. The adaptive learning rate optimization algorithm is performed in these 4 algorithms. In terms of accuracy, the accuracy of the adaptive learning rate BP optimization algorithm is 76.4%, followed by the Newton algorithm and the additional momentum algorithm, and the gradient descent algorithm is the worst.