Coterie is an asynchronous group pattern that finds the group patterns with similar trajectory behavior under unequal time interval constraints. The traditional trajectory pattern mining algorithm often deals with GPS data with fixed time interval sampling constraints, which cannot be directly used for coterie pattern mining. At the same time, the traditional group pattern mining has the problem of missing semantic information, and thus reduces the completeness and accuracy of individualized tourist routes. To address the issue, two semantic-based tourism route recommendation strategies, distance-aware recommendation strategy based on semantics (DRSS) and conformity-aware recommendation strategy based on semantics (CRSS), are proposed in this paper. In addition, with the increasing size of social network data, the efficiency of traditional group model clustering algorithm is of great challenge. Therefore, in order to deal with large-scale social network trajectory data efficiently, MapReduce programming model with optimized clustering is used to mine the coterie group pattern. The experimental results show that the coterie group pattern mining with optimized clustering and semantic information under the MapReduce programming model achieves better recommendation quality than the traditional group pattern travel route in the personalized tourism route recommendation and can effectively handle the large-scale social network trajectory data.