In response to the current intelligent anti-jamming technology's poor performance against rapidly changing interference, a new type of intelligent anti-jamming technology combined with priori knowledge networks is proposed. Firstly, a priori knowledge network is constructed to predict the interference information of the next moment based on historical interference information, enabling the system to better cope with rapidly changing interference; then, reinforcement learning algorithms are employed to achieve online learning of new interference patterns, allowing the algorithm to be applicable to scenarios where the dynamic changes of interference exceed the adaptation range of offline learning models. The simulation comparison between the proposed algorithm and the reinforcement learning algorithm without prior knowledge shows that the proposed algorithm has higher decision accuracy and faster convergence speed when facing rapidly changing interference, and has better adaptability to the environment, which can effectively carry out intelligent anti-jamming.