Agricultural machinery operates in multiple plots, and the cost and efficiency sometimes need to be counted according to the plots. The existing agricultural machinery monitoring system can only record the positioning information and operation status information of agricultural machinery, which is difficult to realize the automatic and accurate division of plots. By studying the attribute characteristics of track points, the uncertainty of the number of work plots and the distribution law of track points were analyzed, and the combination of density clustering method (density-based spatial clustering of applications with noise, DBSCAN) and weak classifier integration algorithm (BP_Adaboost) were used to divide the plots. According to the characteristics that DBSCAN method is effective for most agricultural machinery trajectory points and the recognition error is concentrated, combined with BP_Adaboost method to mine multi-dimensional information association, strong fault tolerance, good classification effect and other advantages. Firstly, DBSCAN was used to obtain the preliminary track point state category, and then the method of BP_Adaboost was used to establish a training model to accurately identify the track point state of agricultural machinery, and divide the land mass according to time series and category markers. The method not only solved the problem of inaccurate clustering only relying on threshold and longitude and latitude information, but also reduced a lot of sample labeling work. Using this method, the accuracy of track point state recognition was 96.75%, and the accuracy of plot division was 97.74%.