The invention discloses an abnormal resident travel mode mining method based on taxi OD data, and belongs to the field of intelligent traffic and data mining. In order to better mine taxi passenger travel rules and deeply mine abnormal modes existing in resident travel, the invention provides a method based on high-dimensional sparse tensor decomposition, namely, multi-dimensional information including time, longitude and latitude, functional area attributes and the like is organized as a tensor model, and low-rank sparse decomposition is performed on the tensor model. Therefore, the key technical problem to be solved comprises the following steps: dividing functional areas for a research area and classifying corresponding data into the corresponding functional areas; organizing corresponding data such as time, longitude and latitude, functional area attributes and the like to form a tensor model; performing low-rank sparse decomposition on the tensor model, respectively extracting a low-rank model and a sparse model, and performing Tucker decomposition; and visualizing a basis matrix obtained through decomposition, and displaying a passenger travel mode visually.