Concerning the problem that traditional intrusion detection method cannot detect the specific attacks aiming at Software Defined Network (SDN) architecture, an intrusion detection model based on Convolutional Neural Network (CNN) was proposed. Firstly, an feature extraction method was designed based on SDN flow table entry. The SDN specific attack samples were collected to form the attack flow table dataset. Then, the CNN was used for training and detection. And focusing on the low recognition rate caused by small sample size of SDN attacks, a reinforcement learning method based on probability was proposed. The experimental results show that the proposed intrusion detection model can effectively detect the specific attacks aiming at SDN architecture with high accuracy, and the proposed reinforcement learning method can effectively improve the recognition rate of small probability attacks.