A mental health monitoring method for college students based on a combined keyword extraction model

With the swift advancement of artificial intelligence technology, intelligent algorithms are increasingly commonly applied within the domain of text processing. Current mental health monitoring methods are inefficient and subjective, and text analysis models extract single keywords, leading to frequent misjudgments and missed judgments. To raise the accuracy of college students' mental health monitoring, a method of college students' mental health monitoring based on combined keyword extraction model was constructed. By designing a combination keyword extraction model composed of mutual information, left and right information entropy layer, difference correlation matrix layer and D_S evidence combination layer, the mental health monitoring system of college students is built. The results show that the average recall rate, the average accuracy rate and the average F1 value of the model are 86.7%, 88.1% and 87.6% respectively. The coincidence rate of hot words in the topic hot words module of the monitoring system is 73.9%, and the delay is 4.6min. The research shows that this method can effectively monitor the mental health of college students and provide accurate means for universities and relevant departments.