Affiliations: Department of Finance and Economics, Tangshan Vocational and Technical College, Tangshan, China | E-mail: [email protected]
Correspondence:
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Corresponding author: Department of Finance and Economics, Tangshan Vocational and Technical College, Tangshan, China. E-mail: [email protected].
Abstract: With the intensification of social pressure and the enhancement of mental health awareness, the mental health issues of college students have become increasingly prominent, attracting social attention. Mental health counseling services, as an important way to alleviate students’ psychological stress, are facing the dual challenges of a shortage of professionals and growing service demands. In recent years, the application of artificial intelligence (AI) technology in the field of mental health has gradually risen, and its advantages in data analysis, pattern recognition, and natural language processing provide new solutions for mental health counseling services. However, existing research still faces problems such as insufficient understanding and limited emotional interaction capabilities in practical applications. This paper delves into the application of AI technology in mental health counseling services for college students and innovates and improves upon the deficiencies in existing research. The study focuses on two main areas: First, word vector generation technologies based on statistics and language models are used according to different application scenarios, and their effectiveness in the analysis of mental health counseling texts is compared. Second, an improved Seq2Seq model is proposed to enhance the emotional understanding and interaction capabilities of emotional dialogue generation algorithms in mental health counseling. This study not only provides technological support for college mental health counseling services but also opens new research directions and perspectives for the application of AI in the field of mental health.
Keywords: Artificial Intelligence (AI), mental health counseling, word vector generation technology, emotional dialogue generation, Seq2Seq model, college students
