Poor university students are a special group. Social development provides many positive factors for poor university students’ personality and psychological development, but negative factors are also accompanied by them, which affect the psychological health of poor university students. University students are in a period of rapid physical and mental development, and it is an important issue that colleges and universities need to solve psychological well-being education. We hope to find out the aspects that can be studied in the irregularity of various factors that affect college students’ mental health. BP neural network is a typical model of artificial neural network. Based on the BP algorithm and the fuzzy comprehensive evaluation of the psychological well-being prediction system for poor university students, this paper systematically summarizes the concept of psychological well-being, the factors that affect psychological well-being, and the related research done by predecessors on psychological well-being. Using the international psychological well-being scale SCL-90 to comprehensively consider the psychological well-being status of poor university students and select the optimized BP algorithm to establish a psychological well-being prediction model, and implement it and compare it with other models to reflect its superiority. Data were collected and analyzed by means of a questionnaire. The regression model was used to analyze the relationship between mindfulness, rumination and psychological well-being. The mediation index fitted by the model reached 0.9. The model can reflect the real situation of the data, that is, rumination plays a partial mediating role in the effect of mindfulness on psychological well-being. The introduction of this psychological prediction model into the psychological well-being education of poor university students not only helps to improve the educational concept and expand the educational approach, but also helps to achieve the goal of psychological well-being education for poor university students, thereby promoting the improvement of the psychological quality of poor university students.

Timely, differentiated distribution of emergency materials reduces flood losses and supports resilient, sustainable cities, and yet demand urgency is rarely quantified jointly with routing. We propose an integrated framework combining game-theoretic AHP–CRITIC weighting, grey relational TOPSIS, exact Fisher–Jenks partitioning, and demand-driven ant colony routing applied to Jiaozuo’s July 2021 “7·20” flood in China. Demand urgency indices for eleven areas span 0.3064–0.6020 and are divided by exact Fisher–Jenks optimization into four grades mapping onto China’s four-tier emergency response system; the grades are robust across weighting schemes (identical under CRITIC-only and equal weights, with only two one-grade shifts under AHP-only weights) and stable under bootstrap perturbation. The routing model incorporates an edge risk exposure term; the optimal route trades an 8.0% increase in adjusted distance for a 6.1% reduction in urgency-weighted delivery time (F* = 235.8774), meeting the local 6 h first-wave dispatch target. At a matched evaluation budget, the improved and standard ant colony algorithms tie in solution quality, and both significantly outperform the genetic and particle swarm algorithms (p < 0.001). Robustness holds across weighting schemes, detour factors, and bootstrap perturbations, supporting resource-efficient, disaster-resilient emergency logistics (SDG 11.5).