Aiming at the shortcomings such as information loss and poor effect of the existing decision tree algorithms for continuous data classification， a Neighborhood Decision Tree （NDT） construction algorithm was proposed. Firstly， the variable-precision neighborhood equivalent granules on the neighborhood decision information system were mined， and the related properties were discussed. Secondly， the neighborhood Gini index measure was constructed based on the variable-precision neighborhood equivalent granules to measure the uncertainty of the neighborhood decision information system. Finally， the neighborhood Gini index measure was used to induce the tree node selection conditions， and the variable-precision neighborhood equivalent granules were used as the tree splitting rules to construct NDT. Experimental results on UCI datasets show that the accuracy of NDT algorithm is generally improved by about 20 percentage points compared with those of Iterative Dichotomiser 3 （ID3） algorithm， Classification And Regression Tree （CART） algorithm， C4.5 algorithm and combining Information Gain and Gini Index （IGGI） algorithm， indicating that the proposed NDT algorithm is effective.