In view of the detection of abnormal behavior of the aircraft, it mainly focuses on positional anomalies and speed anomalies, and lacks the detection of anomalies in the vertical direction of the aircraft. Therefore, the research on the abnormal detection of aircraft climb and descent rate is proposed. The parameter estimation was used to define the aircraft’s climb rate reduction anomaly;the factors affecting the aircraft’s climb rate were analyzed, and the aircraft’s abnormal climb rate was determined;the random forest classifier was trained based on the stratified sampling subset, and the random forest classifier model was used to test the set. A test was performed to determine the category of the test sample. The experimental results show that the model has certain feasibility in aircraft climb rate detection.