The present invention discloses a travel and activity mode identification method based on a BDSCAN clustering algorithm. The method comprises the following steps: cleaning traveler spatio-temporal track data sets that are continuously acquired; calculating an average speed of coordinate points of cleaned data sets, and classifying a position coordinate point whose average speed is higher than a set threshold into a travel mode; based on a DBSCAN clustering algorithm, performing clustering analysis on the cleaned data sets, and determining an activity starting point and an activity ending point according to a clustering result; and according to coordinates and time of data points of an identified travel mode and activity mode, generating a travel time table. According to the method disclosed by the present invention, based on the acquired traveler spatio-temporal track sequence sets, the behavior modes of the travelers are divided into the travel mode and the activity mode by using the density-based clustering algorithm (DBSCAN). The method disclosed by the present invention is convenient for calculation and actual operation and has strong practicality, and by the method, the behavior mode of the travelers can be determined more accurately, so as to facilitate subsequent researches, so that the method has important realistic significance.