In order to reduce the redundant information of hyperspectral image data， optimize the computational efficiency and improve the effectiveness of subsequent applications of image data， a hyperspectral band selection algorithm based on Neighborhood Entropy （NE） was proposed. Firstly， in order to efficiently calculate the neighborhood subset of samples， the Local Sensitive Hashing （LSH） was used as the nearest neighbor search strategy. Then， the NE theory was introduced to measure the Mutual Information （MI） between bands and classes， and minimization of the conditional entropy between feature sets and class variables was used as a method to select effective bands. Finally， two datasets were used to carry out classification experiments through Support Vector Machine （SVM） and Random Forest （RM）. Experimental results show that， compared with four MI based feature selection algorithms， from the perspectives of overall accuracy and Kappa coefficient， the proposed algorithm can select effective band subset within 30 bands faster and achieve local optimization. Some experimental results of the proposed algorithm reach 92.99% and 0.860 8 at the global optimum on overall accuracy and Kappa coefficient respectively， verifying that the proposed algorithm can effectively deal with hyperspectral band selection problem.