The differential pressure air-leakage detection is easily affected by external factors and preset parameters.Aiming at the problem,an air-leakage detection system based on ensemble learning was established,which included the sensor terminal data acquisition system,human-computer interface,and linear fitting of the sensor with the least square method.The Gentle Adaboost algorithm was used to find the best weak classifier in each iteration and update the sample weight in the next round.A strong classifier was formed by integrating the best weak classifier in several rounds of iterations to judge the sealing performance of the tested object.The experimental results show that the accuracy,precision and recall of the proposed system in air-leakage detection are superior to traditional methods and single classification model,and the accuracy is 99.8%.It can effectively overcome the influence of external factors on the test results,and improve the accuracy and stability of differential pressure air-leakage detection.