The application of positive psychology in the work of mental health education in colleges and universities can help students better face setbacks, enable students to face learning and life with a positive attitude, and promote students' future development and promote students' healthy growth. Based on positive psychology, this paper analyzes and discusses its practice and exploration in college students' mental health education.

The psychological health issues of college students are related to their growth and development. Only by implementing psychological health education and maintaining the physical and mental health development of college students can the quality and effectiveness of education be systematically improved. This article briefly analyzes the current situation of psychological health of local normal university students based on their psychological characteristics, and proposes methods for implementing scientific psychological health education for college students in combination with existing problems. The aim is to improve the effectiveness of talent cultivation and contribute to the development of local education.

Objective: To explore the influencing factors of mental health and the mediating role of self-compassion between family cohesion and mental health. Method: Family Cohesion Scale, Symptom Checklist, and Self-compassion Scale were used to investigate 593 college students in Zhejiang Province. Result: Family Cohesion was negatively correlated with mental health and positively correlated with Self-compass- ion among college students; Self-compassion was negatively correlated with mental health. Self-compassion fully mediates the relationship between the two. Conclusion: The path of family cohesion is indirect, and strengthening Self-compassion education can improve the mental health level of college students.

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.