Cognitive radio network is an intelligent wireless communication system which can adjust its transmission parameters according to the environment thanks to its learning ability. It is a feasible and promising direction to solve the spectrum scarcity issue and has become a research focus in communication community. However, cognitive radio network is vulnerable to jamming attack, resulting in serious degradation of spectrum utilization. In this paper, we view the anti-jamming task of cognitive radio as a Markov decision process and propose an intelligent anti-jamming scheme based on deep reinforcement learning. We aim to learn a policy for users to maximize their rate of successful transmission. Specifically, we design Double Deep Q Network (Double DQN) to model the confrontation between the cognitive radio network and the jammer. The Q network is implemented using Transformer encoder to effectively estimate action-values from raw spectrum data. The simulation results indicate that our approach can effectively defend against several kinds of jamming attacks.