To enhance the survivability of unmanned combat aerial vehicles in multi-stage air combat missions,, a data-knowledge driven intelligent avoidance decision-making method was proposed. The method employed Markov decision process to formally model the avoidance decision-making process and introduced the self-attention mechanism of blocked interaction with self-enemy situation based on reinforcement learning. And a data-knowledge driven policy update approach was adopted to train the agent. The avoidance performance and air combat effectiveness of the proposed model were evaluated based on the simulation platform. It was proved that the proposed model has significant theoretical significance and reference value for improving the survivability of unmanned combat aerial vehicles, by comparing it with the traditional reinforcement learning deep Q-network and rule-based maeuvering model.