Software radio technology has become one of the core technologies of modern communication systems due to its high flexibility and reconfigurability. The rapid development of artificial intelligence technology has brought new solutions to the signal processing and transmission quality of software radio systems, especially in the face of complex electromagnetic environments, intelligent algorithms can improve the performance of the system. Aiming at the interference and fading problems of wireless signals in complex electromagnetic environments, this paper designs a deep learning-based intelligent algorithm to assist in solving the bottlenecks in traditional signal processing. The study employs a variety of machine learning models, including convolutional neural networks and reinforcement learning, for signal classification, noise suppression, and channel estimation experiments. The superior performance of AI algorithms in terms of signal decoding, BER reduction, and anti-jamming capability can be verified through simulation experiments. The results show that the signal processing scheme using AI makes significant progress in improving the stability of data transmission and signal accuracy compared to traditional methods, especially with better robustness in dynamic environments. It is demonstrated that the AI-assisted software radio system has enhanced processing capability and has the potential to improve transmission quality in complex environments.