Abstract: The extraction of urban streetscape index is a quantitative approach for urban streetscape research. In combination with existing indices, this research proposes an optimization method for calculating view landscape indices by category using different types of data shown in the panoramic static image, and proposes the frequency of the keypoint neighborhood-scale interval based on scale-invariant feature transform (SIFT) from computer vision. Moreover, the research adopts the hierarchical clustering method to analyze the optimal number of clusters, the degree of contribution of different neighborhood scales, and determine the composition of important indices constituting distribution features. Finally, the research explores the feature effects of different indices on various neighborhood scales. Research results show that, view landscape indices have different neighborhood scale effects. Furthermore, for the indexes studied, the green view index and sky view factor (spatial composition level), the frequency of the keypoint neighborhood-scale intervals (0, 10 and (10, 20 (object scale level), and the color richness index (color level) are important feature indices that constitute urban street distribution features. Determining the distribution of urban street features at various neighborhood scales can provide reference for landscape quality improvement, quantitative management, and urban micro-renewal of urban street spaces.