## 5 Conclusions  
This study establishes a quality evaluation framework that encompasses both subjective and objective dimensions rooted in urban design theory, facilitates high-precision analysis of streetscape images through big data and machine learning, and ultimately conducts an analysis of urban-scale accessibility and quality grounded in human behavior. The pathway consisting of \"quality measurement, quality evaluation, overlay analysis, and improvement strategy\" effectively enhances the relevance and operability of urban renewal design grounded in big data. Upon comparing the results of the comprehensive measurement of street spatial quality and accessibility, alongside the spatial characteristics of various street types in Mingcheng District, it is observed that the spatial distribution of street quality exhibits characteristics of \"single-core belt extension and edge radiation\". Simultaneously, the spatial quality and accessibility of streets demonstrate clear relationships with street hierarchy, indicating that the overall value of main roads surpasses that of secondary roads and side streets. Ultimately, this paper conducts a comprehensive evaluation of the streets utilizing the \"quality-accessibility\" evaluation matrix classification features, visually interpreting street conditions, selecting representative streets for case studies, and proposing renewal strategies focused on detail optimization, potential activation, quality enhancement, and comprehensive renovation. In comparison to traditional quality evaluation methods, the large-scale measurement and assessment of street quality based on streetscape images significantly enhances efficiency and offers advantages in comprehensiveness, universality,and generalizability. Moreover, in contrast to traditional street renewal pathways, the categorization of streets that integrates accessibility and spatial elements allows for a more precise understanding of the street renewal sequence, thereby facilitating the enhancement of street spatial quality in a more systematic and orderly fashion. However, limitations in data sources mean that street view imagery is predominantly captured from the perspective of vehicles, thereby overlooking the pedestrian experience of daily commuters. Furthermore, the low resolution of street view data excludes critical spatial quality factors, such as \"neatness\" and \"sense of ambiance\", resulting in less accurate quality evaluations. Advances in technology and the emergence of more open data environments could enable more precise and detailed investigations into street spatial quality. Moreover, sDNA accessibility-based analysis methods overlook the functional attributes and urban spatial structure of the surrounding areas. Building on this foundation, this study addresses the identified deficiencies by integrating variables to refine index selection, resulting in a more comprehensive and objective evaluation model. Future research should incorporate the functional diversity and usage frequency of street areas to enable a more holistic evaluation of street quality and accessibility while aiming to categorize and address street-related issues with greater specificity. Future research is expected to develop a comprehensive evaluation and optimization framework, facilitating the application of the model to large-scale urban spatial renewal projects. Conceptualization, J.C., Y.T. and W.Z. ; methodology, J.C. and Y.T. ; software, Y.T. and W.Z. ; validation, J.C. and Y.T. ; formal analysis, J.C.; investigation, J.C. and Y.T. ; resources, Y.T. and W.Z. ; data curation, J.C. and Y.T. ; writing--original draft preparation, J.C. and Y.T. ; writing--review and editing, J.C. and W.Z. ; visualization, J.C. and Y.T. ; supervision, J.C. and W.Z. ; project administration, W.Z. ; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Fundamental Research Funds for the Central Universities, grant number 300102414105. Not applicable.