This paper focuses on the assessment method of athletes’ mental health and explores the effective path of intervention for athletes with abnormal mental health status. A BP neural network model was established to realize the assessment of athletes’ mental health status, and a genetic algorithm (GA) was used to optimize the limitations of BP neural network in training. Athletes were selected as subjects, and the mental health status of athletes was assessed by the symptom self-assessment scale (SCL-90) combined with the GA-BP neural network assessment model in this paper. Further, the athletes whose model assessment results showed the existence of abnormal mental health status were taken as research subjects, and psychological intervention experiments were conducted for them by using positive thinking training, and the experimental results were analyzed by visualization. The study shows that the GA-BP neural network assessment model can reach 86.67% of the assessment accuracy on the test set. After adopting this paper’s positive thinking training intervention, the athletes’ five-factor positive thinking questionnaire (FFMQ) scores, attention level and sports performance level were improved to different degrees, and the follow-up experiments showed that the continuation of the intervention effect was more significant overall. The research results of this paper can provide an effective reference for timely grasping the mental health status of athletes and taking scientific intervention measures.