Abstract: Aluminum alloy thin-walled components are widely applied in aerospace and automotive manufacturing industries due to their excellent lightweight characteristics. However, their low structural rigidity renders them highly susceptible to milling chatter during machining. Traditional chatter monitoring methods rely on manual feature selection and threshold configuration, which suffer from inherent limitations such as feature redundancy, strong subjectivity, and poor adaptability to varying working conditions, thus failing to guarantee reliable monitoring accuracy. To address the aforementioned issues, a novel milling chatter monitoring method based on multi-scale feature fusion and a triple squeeze-and-excitation mechanism is proposed. Firstly, continuous wavelet transform (CWT) is employed to conduct multi-scale time-frequency analysis on vibration signals, and the resulting time-frequency images are taken as the input of the model. Secondly, multi-scale deep features extracted by a four-layer convolutional network are fused to enrich the dimensionality of feature information. Finally, a triple squeeze-and-excitation attention mechanism is introduced to adaptively recalibrate feature weights and enhance the model’s capability of capturing critical chatter features. Experimental results indicate that the proposed method achieves a chatter recognition accuracy of 99.1%, enabling accurate adaptation to complex machining conditions. It effectively overcomes the deficiencies of conventional monitoring approaches and provides robust technical support for intelligent chatter monitoring in thin-walled component milling.