The invention discloses a deep Q neural network anti-interference model and an intelligent anti-interference algorithm. A pair of emitting end and receiving end is a user, communication with the useris carried out, user communication is interfered by one or multiple jammers, the spectrum waterfall map of the receiving end is used as an input state of learning, and frequency domain and time domaincharacteristics of the interference are calculated. The algorithm comprises steps that firstly, a Q-value table corresponding to fitting is obtained through the deep Q neural network; secondly, a strategy is selected by the user according to the probability, training is carried out based on a return value of the strategy and the next environmental state, and the network weight and frequency selection strategy are updated; when the maximum number of cycles is reached, the algorithm ends. The model is advantaged in that the model is complete, the physical meaning is clear, the design algorithmis reasonable and effective, and the anti-interference scene based on the deep reinforcement learning algorithm can be excellently described.