The intelligent traffic sign recognition system is a smart transportation environment perception solution developed using deep learning and computer vision technologies. This project addresses the needs of autonomous and assisted driving systems for road environment perception by accurately identifying traffic signs, signals, and lane markings through innovative algorithm design and technology integration. The system employs an improved Faster RCNN algorithm framework, achieving a traffic sign detection accuracy of 92.7%. It optimizes computational efficiency to meet real-time processing requirements. Innovatively, the system combines traditional image processing techniques with deep learning methods, simplifying computational processes through threshold segmentation and significantly enhancing operational efficiency. A comprehensive simulation testing environment has been established to provide an efficient platform for algorithm verification and optimization. This solution not only improves the environmental perception capabilities of intelligent driving systems but also offers reliable technical support for smart transportation construction, highlighting its significant application value. In practical road tests, the system demonstrates excellent adaptability and stability, effectively handling various complex road scenarios and providing robust assurance for vehicle safety. The promotion and application of this achievement will help advance the development of intelligent transportation industries and enhance road safety levels.