Linear local tangent space alignment（ LLTSA） is a dimensionality reduction method which is easily used to pattern recognition. However,it is an unsupervised dimensionality reduction method and only use global neighborhood parameter,when it used to high-dimensional data for dimensionality reduction,its incapacity of using part sample class label information and self-adaptive adjust neighborhood parameter while the samples space distribution changed. Aiming at the problems above,a semisupervised neighborhood self-adaptive linear local tangent space alignment（ SSNA-LLTSA） dimensionality reduction method is proposed in this paper. In SSNA-LLTSA, the distance between different points is adjusted by utilizing part class label information,thereby a new distance matrix is formed and the neighborhood is constructed through this new distance matrix. At the same time, the neighborhood parameters are self-adaptive adjusted according to probability density of each sample point neighborhood. The experiment results of classical 3D manifold,UCI datasets and bearing fault diagnosis show that the algorithm overcomes the drawbacks that the LLTSA has no supervision and the use of global unified neighborhood parameters and it is more effective to find the low dimensional nature of the data for improving the recognition accuracy and has certain superiority.