Aiming at the problem of limited wireless sensor network resources and intrusion detection system strategy optimization, this paper proposes a wireless sensor network intrusion detection method based on complex network evolutionary game. Combined with the small world model theory, the connection relationship between network nodes is simulated, and the network connectivity is enhanced and the transmission energy consumption is reduced without changing the original relationship of nodes. Then, the attack and defense game model of wireless sensor network about cluster head nodes and malicious nodes is constructed. The node income is calculated by the income matrix, and the reward and punishment mechanism are used to describe the income change of nodes choosing different strategies in the game process. At the same time, the empirical weighted attraction learning algorithm is introduced to improve the strategy update rules of the traditional game and the algorithm is applied to the intrusion detection system, so that the cluster head nodes can dynamically update the strategy selection and obtain the optimal strategy of intrusion detection under different conditions. The experimental results show that compared with the traditional method, the diffusion depth of the cluster head node detection strategy of the proposed algorithm can reach 79%. Under this algorithm, the cluster head nodes choose to detect the attacks in the sensor network as much as possible while ensuring its own detection income, so as to ensure the network detection rate and reduce the consumption of various resources in the network.