Cutting chatter can lead to problems such as poor surface quality of the machined workpiece, reduced material removal rate and increased tool wear. The tool tip modal parameters are essential inputs for constructing stability lobe diagrams and selecting chatter free machining parameters. However, in the machining process, the tool tip modal parameters change with the tool pose and the tool changes frequently, and the classical impact test method has low efficiency and high cost, so how to accurately and efficiently predict the tool tip modal parameters under the changed pose has become an urgent problem to be solved in the cutting process. In this paper, combined with the idea of transfer learning, a method of modal parameter prediction based on multi-source transfer learning is proposed. When a new tool is used, the tool point modal parameters under only a few positions need to be measured through impact test, and then the tool point modal parameter prediction model for the new tool can be obtained by multi-source transfer combined with the modal parameter data of multiple existing tools. Finally, a practical experiment on a five-axis machine tool shows that the proposed method is effective.