To address the insufficient spatio-temporal feature perception caused by neglecting historical traffic information in existing methods,this study proposes an intersection signal control method integrating deep reinforcement learning with spatio-temporal feature modeling. The approach employs a hybrid D3QN-LSTM network architecture,which encodes multi-period traffic information into high-dimensional matrices through discrete traffic state representation. A convolutional neural network extracts spatial features,while a long short-term memory network captures temporal dependencies. A reward-feedback-driven dynamic exploration mechanism is further designed to optimize policy training. Experiments conducted on the SUMO simulation platform demonstrate that during morning peak traffic,the proposed method reduces average queue length by 49.95%,35.04% and 16.72%,and decreases cumulative waiting time by 63.03%,35.55% and 20.15% compared to fixed-timing control,conventional reinforcement learning methods and D3QN,respectively,validating the superiority of spatio-temporal feature modeling and dynamic exploration strategies. To assess algorithmic robustness,off-peak traffic flow experiments further confirm that the proposed method maintains significant advantages in both average queue length and cumulative waiting time metrics,demonstrating strong adaptability and generalizability across varying traffic load conditions.