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A Differential Evolution Extreme Learning Machine (DE-ELM) algorithm based on flight data is proposed to solve the problem of fault evaluation of an aircraft’s control system. The algorithm combines Differential Evolution (DE) and Extreme Learning Machine (ELM), by training the flight data, a black box model of aircraft control system is constructed. Because the input weights and hidden layer thresholds of the ELM are generated randomly, the randomness of ELM is large and the stability of ELM is not high. Therefore, the input weights and hidden later thresholds of ELM are optimized by DE, which has strong optimization ability, so that the structure of ELM can be optimized and the stability and robustness of ELM can also be improved. The simulation results show that the decisive coefficient of DE-ELM reaches 97.6%, and its mean square error is reduced by 79% compared with BP neural network and 64% compared with ELM. Therefore, this method can effectively improve the accuracy and has better generalization performance.