A Big Data-Driven Psychological Early Warning System for College Students Using Multi-Channel Active Noise Reduction and Adaptive FxLMS Algorithms

Abstract

To address the challenges posed by the frequent occurrence of mental health issues among college students and the limitations of traditional early warning systems, such as delayed response and partial data coverage, this study aims to construct a psychological early warning mechanism driven by big data analysis technology. By designing a multi-level active noise reduction control system and an improved variable step-size CFxLMS algorithm, and integrating stress event parameters from social media with personality trait characteristics, a multi-source data collaborative emotion prediction model is established. Simulation experiments show that the system achieves an accuracy rate of 94.2% and an F1 score of 94.1% in psychological crisis identification, which is 4.6% higher than that of the traditional LSTM model. Furthermore, its performance fluctuation under noise interference is only 3.2%, and the response time is optimized to 120 milliseconds, effectively supporting concurrent processing for tens of thousands of users. The results indicate that the proposed method significantly improves early warning accuracy and real-time performance, addressing the bottleneck of insufficient tracking ability for non-stationary psychological signals. To sum up the above, this study innovatively combines active noise reduction technology from control theory with the psychological stress-cognition model, providing a scalable and highly robust active early warning solution for college mental health management. It achieves a paradigm shift from passive intervention to real-time prediction and establishes a reproducible technical benchmark in the field of intelligent psychological monitoring.