The mixed manifold spectral clustering adaptive segmentation method is proposed, while the laser point cloud is regarded as a linear and nonlinear mixed manifold in three-dimensional Euclidean space. The mixture probabilistic model is constituted by M principal component analyzers, which are constructed by the mixtures of probabilistic principal component analysis method, and the adjacency matrix of point cloud is obtained. In the spectrum space, the geometrical characteristics of point cloud segmentation are embedded in the dimension, and the multi-dimensional vector, which describes the characteristics of point cloud classification, is obtained by N-cut method. The between-within proportion algorithm is adopted to segment point cloud adaptively. Experimental results show that the proposed algorithm can obtain segmentation results that converge to the geometric features with the probability larger than 80% in wide range of preset parameters. Moreover, it performs stable with Gaussian noises of 0 mean, 0.01 standard deviation and compound noise of 0.25 times the total points. The proposed method shows good noise resistance.It is applied to point cloud of satellite model acquired by slice laser three-dimensional imaging and achieves good segmentation results.