Digital Signal Processing and Artificial Intelligence for Automatic Learning Guoqiang Li, Yinfa Wang 4 4 2025 https://doi.org/10.6025/dspaial/2025/4/4/155-161 https://www.dline.info/dspai/fulltext/v4n4/dspaiv4n4_2.pdf The paper proposes an integrated safety assessment algorithm for road construction by combining the Analytic Hierarchy Process (AHP) and Back Propagation (BP) Neural Network. Traditional safety evaluation methods, such as fuzzy comprehensive evaluation, lack adaptability to dynamic environmental changes and struggle to update weights in real time. To address these limitations, the authors leverage AHP for systematic determination of indicator weights and the BP neural network for its strong nonlinear modeling, self learning, and adaptive capabilities. Four key safety indicators geological disasters, hazardous sources, hidden dangers, and compliance with safety standards are selected for evaluation. The AHP method quantifies their relative importance using expert scoring and pairwise comparisons. At the same time, the BP neural network processes input data through a three-layer architecture to compute a safety risk coefficient. This coefficient maps to one of five predefined safety risk levels, enabling practical risk categorization. Experimental validation on a real infrastructure project demonstrates that the integrated algorithm significantly outperforms conventional approaches, achieving an average relative error of only 0.004 compared to 0.0468 for traditional methods. Results confirm the model’s high accuracy, reliability, and effectiveness in predicting safety risks. Despite its advantages, the authors acknowledge remaining limitations and call for further refinement to better align with public safety expectations. The study contributes a robust, data driven framework for improving safety management in complex road construction environments.