The invention discloses a turning chatter detection method, and relates to the technical field of detection. In the turning process, the state of a machine tool can be reflected in dynamic cutting force. The turning chatter detection method includes the steps that firstly, an off-line data training model is used, force signals are decomposed to a sixth layer through wavelet packet transformation, energy of each node is worked out, and a 64-dimension feature vector is obtained; dimensionality reduction is conducted on the feature vector through least squares support vector machine-regression feature elimination (LSSVM-RFE), redundancy features are eliminated continuously, optimal features are selected out, and a least squares support vector machine classifier is trained according to the optimal features; and each selected feature corresponds to one wavelet packet node, in the on-line detection process, only a small wavelet packet matrix is needed to decompose force signals to the small wavelet packet nodes selected in the off-line training process, the feature vector is built and input into the classifier, and a detection result is obtained. By the adoption of the dimensionality reduction method, the turning chatter detection method has the beneficial effects of being high in speed and high in identifying accuracy and effectively guaranteeing the machining safety and the product quality.