As an important part of structural dynamic analysis, modal parameters are the key to chatter prediction during milling of thin-walled components, and machine learning provides a new paradigm for traditional identification of structural modal parameters. However, for complex curved thin-walled parts, it is difficult to obtain the data in a specific environment and the amount of data collected would be large; uncertain factors such as high-dimensional nonlinear mapping relationships would affect the complex curved thin-walled parts as well. Therefore, a new method based on machine learning is proposed to identify the dynamic characteristics of curved thin-walled parts during milling process. Firstly, the state space model of curved thin-walled milling system is established, the continuous system is discretized, and the stochastic state space equation of generalized milling system discretized is derived. Secondly, based on the random subspace theory, modal parameters of the milling process of curved thin-walled parts are obtained. Then, the sliding window technology is used to reduce dimensionality of the data, extract the signal features, and establish the functional relationship between the input features and modal parameters through the neural network for modal parameter recognition, therefore, to realize the modal parameter recognition of curved thin-walled parts. Finally, milling dynamic parameters of the S-shaped standard part are obtained by using the method proposed in this study and analytical method, verifying accuracy of the proposed method.