The α-shape algorithm is not suitable for surface reconstruction of scattered and non-uniformly sampled points. In order to solve the problem, an improved surface reconstruction algorithm with adaptive α-shape based on Local Feature Size (LFS) of point cloud data was proposed. Firstly, Medial Axis (MA) of the surface was approximated by the negative poles computed by k-nearest neighbors of sampled points. Secondly, the LFS of sampled points was calculated by the approximated MA, and the original point clouds were unequally simplified based on LFS. Finally, the surface was adaptively reconstructed based on the radius of circumscribed ball of triangles and the corresponding α value. In the comparison experiments with α-shape algorithm, the proposed algorithm could effectively and reasonably reduce the number of point clouds, and the simplification rate of point clouds achieved about 70%. Simultaneously, the reconstruction result were obtained with less redundant triangles and few holes. The experimental results show that the proposed algorithm can adaptively reconstruct the surface of non-uniformly sampled point clouds.