More and more mobile computing applications rely on location information to provide locationbased services, and outdoor positioning technology for mobile devices is critical. The widely adopted method is currently GPS, but the GPS position information of the mobile device is dependent on the GPS sensor of the mobile device such as a mobile phone. Although telecom operators provide users with calling and data services, they cannot obtain the users’ precise GPS positions.In view of this situation, we propose to use the connection signal data (telecommunication data for short) between the mobile terminal and the telecom base station to achieve the positioning service of the mobile device. Considering that telecom operators have accumulated vast amounts of telecom data, it is possible to allow operators to acquire user locations by studying outdoor positioning technologies based on telecom data. This paper extracts the telecom feature data, takes the GPS location of the mobile phone as the tag data, and studies five d outdoor positioning algorithm based on machine learning model. The GPS coordinate points are successfully predicted from the base station signal data. A large number of experiments are carried out to compare positioning accuracy,computation time, positioning accuracy of different data collection modes, positioning accuracy of different features among these algorithms. Besides, the effect of postprocessing on positioning accuracy is explored. Finally, the experiments show that the random forest classification model based on gridding is the best model and can achieve an average error of 15 to 20 meters and a median error of 10 meters. Compared with the previous regression algorithms, the proposal improves the accuracy by 39.46% and 54.28% on the 2G and 4G data respectively, thus achieving the positioning accuracy close to GPS positioning.