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Aircraft air-conditioning system plays a vital role for both aircraft and passengers. Predicting the health evaluation of aircraft QAR (Quick Access Recorder) air-conditioning data can ensure the flight comfort and safety of passengers and crew, and can also improve equipment work stability to avoid flight delays or cancellations caused by equipment failures. In order to improve the prediction accuracy of the SVM model of air-conditioning system state monitoring, an SVM air-conditioning state evaluation method based on the particle swarm algorithm is proposed. The experimental results show that the proposed method can effectively evaluate the state of the air-conditioning system by using the sample data collected by the A320 aircraft air-conditioning system state monitoring to conduct predictive analysis.