Due to the complicated interference pattern of the infrared decoy and the variable shape of the target maneuver, the traditional proportional guidance law is easily interfered. In order to improve the performance of missiles using the proportional guidance method, an intelligent guidance law that uses the RBF(radial basis function) network to control the proportional coefficient and the timing of missile launch is proposed. Taking the flight time and miss distance as reference, the weighted index function is used to transform the optimal proportional coefficient and launch timing problem into a single objective optimization problem. The quantum particle swarm optimization algorithm is introduced to solve the optimal decision parameters which are used as the network output, and the interference pattern is used as the network input to train the RBF network offline. To improve the training efficiency, the RBF network is initialized by combining the Kmeans and KNN(K nearest neighbors) algorithms. Simulation results show that the intelligent guidance law is better than the extended proportional guidance law and the adaptive sliding mode guidance law when there is infrared decoy interference.