Abstract: The axial power deviation of a reactor can reflect the axial power distribution of the core and the operation of the reactor. Aiming at the difficulty of predicting the axial power deviation under variable working conditions, this paper proposes a method based on the combined feature screening and temporal convolutional network. TCN) reactor axial power deviation prediction method. Taking the basic principle of axial power deviation control as the starting point, this paper analyzes the factors affecting the change of axial power deviation, comprehensively analyzes the redundancy and correlation among multi-dimensional features, uses the combined feature screening strategy to form the optimal feature subset for axial power deviation prediction, constructs the key correlation feature data for axial power deviation prediction, and inputs it into TCN to capture dynamic causality. To achieve the prediction of reactor axial power deviation. Experimental studies show that the proposed method can deeply excavate the temporal causal change characteristics of the parameters related to the axial power deviation of the reactor, accurately predict the development trend of the axial power deviation, solve the problem that the traditional prediction model does not predict and track in time under complex working conditions, and provide an auxiliary reference basis for the reactor status monitoring and safe operation of nuclear power plants.