Similarity join is a fundamental operation for managing semantic trajectory data. In massive data scenarios, distributed paradigms can be utilized to process similarity joins for huge amounts of semantic trajectory data, but they face the challenge of locally aware partitioning of the data. To address this problem, we propose a distributed similarity join framework based on trajectory segments. The semantic trajectory data are partitioned by segments, and semantically similar trajectories are stored in the same partition to improve the local similarity of the partitions. We design global and local indexes to efficiently manage partitioned data. We develop a filtering validation framework to enhance similarity query performance by pruning irrelevant trajectories based on the temporal, spatial, and semantic distances of trajectory segments. Extensive experiments on three real-world datasets demonstrate that our method achieves superior scalability and query efficiency, compared to other methods.