Provided is a point cloud attribute compression method based on a KD tree and optimized graph transformation, wherein same, with regard to point cloud data, reduces the influence of a sub-graph issue on the graph transformation efficiency by means of a new transformation block division method, optimizes a graph transformation kernel parameter, and improves the compression performance of the graph transformation, and comprises: point cloud pre-processing, point cloud KD tree division, graph construction in the transformation block, graph transformation kernel parameter training, and a point cloud attribute compression process. The present invention optimizes the division method for a point cloud transformation block, and makes the number of points in the transformation block the same, and also realizes that the dimensionality of a transformation matrix is basically the same, so as to facilitate parallel processing of subsequent graph transformations; also optimizes the graph establishment in the transformation block, and avoids the sub-graph issue caused by the existing method; and at the same time optimizes, by training the kernel parameter of the graph transformation, the sparsity of a graph transformation Laplacian matrix, so as to achieve a better point cloud attribute compression performance.