Automatically recognizing surgical workflow plays a significant part in improving surgical training efficiency by providing automated skill assessment for surgeons. Based on a deep model(SV-RCNet) which mainly consists of a deep residual network(ResNet) and a long short term memory(LSTM) network, our framework introduced reinforcement learning method into surgical workflow or phase recognition for the first time and is evaluated on cholec80 dataset which contains 80 videos of cholecystectomy surgeries. In our framework, an intelligent agent is trained using Markov Decision Process (MDP) model and Proximal Policy Optimization(PPO) algorithm with discriminative spatio-temporal features extracted from the SV-RCNet as input. Experiments on cholec80 dataset have outperformed the SV-RCNet in terms of accuracy, precision and recall.