In intelligent control, artificial intelligence (AI) pattern recognition further demonstrates the convenience of human-machine interaction in operational processes. By combining control theory with recognition technology, speech recognition can more accurately control the parameters and state of the model, thereby improving the accuracy and robustness of the model. With the introduction of reinforcement learning systems, speech recognition patterns can autonomously learn and optimize model parameters through continuous trial and error and feedback, achieving more efficient speech recognition. Facing the introduction of speech recognition functions in various AI devices, the adoption of a distributed computing mode can not only solve the problem of device intelligence but also improve the accuracy of speech recognition. By using a multi-step temporal difference method to update the correlation program between speech data and iterating optimization with neural networks, the cloud interconnection mode of devices can be initiated to store and back up adequate information reasonably. Combining intelligent control with reinforcement learning attached to the speech recognition process reduces training costs and complexity, providing a new research approach for the recognition technology of massive texts.