The collision between sea ice and ship is a serious threat to navigation safety. However, the idea of using vibration data generated from ship-ice collisions to obtain physical information about sea ice, such as its type and thickness, is a novel method. This method overcomes the problem of traditional techniques (optical cameras and satellite remote sensing) being affected by visibility, and also provides data for assessing the ice load capacity of ships. In this paper, we propose a new method for sea ice type and thickness identification based on vibration sensor networks and machine learning. Firstly, the characteristic data set of sea ice type and thickness is constructed by analyzing the six nodes ice-ship vibration signals obtained from the vibration sensor network. We then used six classification algorithms to identify three common sea ice types (broken ice, flat ice, sea/open water) and assessed the accuracy of these algorithms at each node. Then, we use Gradient Boosting Decision Tree (GBDT) regression algorithm to predict sea ice thickness and evaluate the combination of node features. The results shows that the accuracy of single-node sea ice classification is 98.71%, and the R ² for sea ice thickness regression on a single node is 0.908. After feature fusion of two nodes, the R ² for sea ice thickness regression is 0.94, with a Root Mean Square Error (RMSE) of 0.847 cm, and the accuracy is better than traditional identification methods. The results show that the sea ice type and thickness can be identified with high accuracy by analyzing the vibration signals generated by ice-ship collision. The reliability and effectiveness of the proposed method in low visibility and other environments are verified by the sailing experiments in the Bohai Sea ice area accumulated at 1020 nautical miles.