Under the condition of variable rotating speed, it is difficult to extract the degradation characteristics of the axial piston pump, which also reduces the accuracy of degradation recognition. To address these problems, this paper proposes a degradation state recognition method for axial piston pumps by combining spline-kernelled chirplet transform (SCT), adaptive chirp mode pursuit (ACMP), and extreme gradient boosting (XGBoost). Firstly, SCT and ACMP are proposed to deal with the vibration signal instability and high noise of the axial piston pump under variable rotating speed. The instantaneous frequency (IF) of the axial piston pump can be extracted effectively by obtaining the accurate time-frequency distribution of signal components. Then, stable angular domain vibration signals are obtained by re-sampling, and multi-dimensional degradation characteristics are extracted from the angular domain and order spectrum. Finally, XGBoost is used to classify the selected characteristics to recognize the degradation state. In this paper, the vibration signals in four different degradation states are collected and analyzed through the wear test of the valve plate of the axial piston pump. Compared with different pattern recognition algorithms, it is verified that this method can ensure high recognition accuracy.