Subspace clustering algorithm based on low-rank representation (LRR) cannot handle large-scale data effectively，and distributed low-rank subspace clustering algorithm (DFC-LRR) cannot handle the high-dimensional data directly． To solve this issue，a distributed low-rank subspace clustering algorithm based on tensor and distribu- ted computing was proposed． The proposed method firstly considered high-dimensional data as tensor and extended LRR subspace clustering algorithm to high-dimensional data by introducing tensorial multiplication into self repre- sentation of data． Then the low-rank coefficient tensor was obtained through the distributed parallel computing，and get the sparse similarity matrix by sparing every lateral slices of the coefficient tensor． Experimental results on the Extended Yale B，COIL20 and UCSD datasets show that the proposed algorithm outperforms DFC-LRR in clustering accuracy，and distributed computing can reduce the running time obviously．