Abstract: The street network morphology has a significant impact on the street spatial quality in historical and cultural towns. Taking Chunxi Town, the only national-level historical and cultural town in Nanjing, as an example, this study first calculated the Network Quantity Penalized by Distance（NQPD） and Two Phase Betweenness（TPBt） indices of the street network morphology based on the Spatial Design Network Analysis（sDNA） model. Secondly, high-precision semantic segmentation of crawled street view images was performed using the DeepLabV3+ model to quantify the street spatial quality. On this basis, the study explored the influence mechanisms of the street network morphology on the spatial quality using Ordinary Least Squares regression and Geographically Weighted Regression models. By combining the spatial distribution patterns of historical and cultural Points of Interest（POI）, it identified streets that urgently require prioritized management or demonstration and promotion. The results shown that: NQPD and TPBt exhibited the same spatial distribution characteristics. Green view index and Interface Enclosure Index followed a ’high at the center, low at the periphery’ pattern, whereas Sky Openness Index followed the opposite pattern, and Motor Vehicle Disturbance Index shown a ’low concentration, high dispersion’ pattern. The impact of NQPD and TPBt on spatial quality of local streets followed two distinct patterns: a ’trade-off ’ model and a ’synergistic’ model. The spatial distribution of historical and cultural POIs shown a ’local concentration, overall dispersion’ pattern. Streets that urgently needed prioritized management or demonstration and promotion were mainly located in the central and southwestern areas of Chunxi Town. The findings offer scientific evidence to support the planning, renovation, and management of streets in Chunxi Town and other historic cultural towns.