Concerning the problems that the determination of fixed scale in 3D point cloud feature point detection algorithm requires empirical knowledge,and the calculation of adaptive scale requires much time cost,an improved algorithm named Adaptive Neighborhood Intrinsic Shape Signature(ANISS)was proposed. Firstly,the local features were used to calculate the adaptive neighborhood k value of each point. Then,the k value was used as the neighborhood size of the ANISS algorithm,and by comparing the ratio of the continuous eigenvalues with the threshold,the approximate feature points were obtained. Finally,the k values of the approximate feature points were used as the neighborhood size of the Non-Maximum Suppression(NMS),and the NMS algorithm was executed to obtain the final feature points. The results of rotational translation invariance experiment and noise sensitivity experiment show that the repeatability of the feature points detected by ANISS algorithm is higher than that of Intrinsic Shape Signature(ISS)algorithm. ANISS algorithm not only reduces the inaccuracy caused by the neighborhood parameter input in ISS algorithm,but also has high computational efficiency.