With the rapid development of Chinese society and economy as well as the deepening of the reform of the higher education management system and the change of employment mode of graduates, college students face various challenges of frustration and pressure in the areas of value and ethical concepts, interpersonal relationships, behavior, life, and employment. Some students who are relatively fragile psychologically are unable to bear the heavy pressure of frustration and challenges, and are prone to psychological crisis, overreacting, and even hurting others or self-injury or suicide. How to solve the current psychological problems of college students and help them become adults and talents is a new task and a serious challenge for college students’ mental health education under the new situation. With the development of the Internet, more and more people are expressing their emotions in social networks, including suicidal intentions, which creates new opportunities for suicide prevention. If suicide risk can be automatically identified using microblogs, it can open up new directions for suicide prevention efforts. This paper is based on the use of deep learning to build a social media suicide identifier to explore the possibility of assessing individual users’ suicide in real time through social platforms. To verify the effectiveness of this algorithmic model, the keyword attributes used by the algorithm are statistically analyzed and compared with the prediction results of two other algorithmic models. The experimental results show that the algorithmic model based on deep learning can be more effective in predicting the suicide risk of microblog users.

Occupational identity is an individual’s view, recognition, and approval of his long-term occupation, and its importance to every professional is self-evident. Only when a professional person agrees with the profession he is engaged in from the bottom of his heart can he devote himself wholeheartedly to it and unreservedly exert his greatest potential. On the basis of sorting out and analyzing the prevailing theoretical and empirical research results, this paper deliberates the empirical research on the influence mechanism between employees’ occupational identity and occupational well-being. In this study, through big data analysis, literature search, questionnaire survey, and other methods, this paper obtained the professional identity data of employees in different companies and used a method of big data analysis, namely, BP neural network (BPNN) to design in this paper to verify the data, and finally obtain an effective theoretical model of the influence mechanism of occupational identity and occupational well-being. The main work of this paper is as follows: (1) it introduces the interpretation of the concept of “professional identity” by different scholars at home and abroad and makes a brief review of the researches on professional identity and professional well-being made by foreign scholars in recent years. (2) The basic knowledge and algorithm process of artificial neural network (ANN) are introduced, and the design of the evaluation model of the influence mechanism of occupational identity on occupational well-being based on BPNN is proposed. (3) The simulation software validates the neural network (NN) assessment system developed in this paper. Experiments reveal that the BPNN system is a reasonable and feasible evaluation approach for analyzing the impact of occupational identity on occupational well-being.