In modern engineering, the importance of risk management in decision-making has increased proportionally with the growing scale and complexity of projects. Construction engineering projects are frequently faced with various risks, including insufficient financial support, human resource allocation difficulties, supply chain disruptions, etc., which may cause project delays, cost overruns, or even failure. Therefore, effective evaluation and mitigation of risks in different construction phases are crucial to ensuring project smooth progress. This study focuses on the challenge of intelligent risk assessment in construction engineering and proposes a novel evaluation model integrating deep neural network (DNN) modeling with intelligent decision calculus. The framework first uses the Analytic Hierarchy Process (AHP) to quantitatively measure phase-specific evaluation indicators, then performs feature extraction through Temporal Convolutional Networks (TCN). Reinforcement learning (via Deep Q-Networks, DQN) is incorporated to enhance the model’s interactive decision-making ability, realizing dynamic risk identification. Experimental results show that the model achieves an identification accuracy of over 80% in distinguishing low, medium, and high-risk scenarios, demonstrating an innovative approach to construction risk assessment that significantly improves decision-making efficiency.