With the vigorous development of mobile internet technology and the widespread use of smart devices represented by mobile phones, crowdsourcing mobile trajectory collection has emerged as a significant means of data acquisition due to its efficiency, flexibility, data diversity, and low cost. However, variations in the types of intelligent terminals and user habits have led to inconsistent quality in the crowdsourcing data obtained. Especially in complex environments with signal shielding, the reliability of location precision is low, making it difficult to meet the application needs. Addressing these issues, this paper analyzes and extracts multiple features from crowdsourcing multi-source observation data collected by various models of intelligent terminals (such as Xiaomi and HUAWEI). In open scenarios, it employs multi-source fusion methods based on trajectory segmentation and clustering for data processing. In shaded environments, it utilizes map-matching algorithms to correct errors, thereby enhancing positional accuracy. Field tests and validations have demonstrated that the positioning accuracy of crowdsourcing data has been improved by the algorithm proposed in this paper. This proves the feasibility and effectiveness of this method, which can provide assistance in the analysis and processing of crowdsourcing data.