以往的视觉效果研究主要集中在对城市整体环境的评估，而缺少针对城市内部历史街区的研究。为评价历史街区的植物景观视觉效果，结合街景图像和机器学习方法进行分析。通过比较不同模型的性能，选取了ResNeSt模型应用于植物景观协调程度和健康程度的评价研究中。结果表明，ResNeSt模型在分类和回归任务中性能最佳，其评分结果与专家评分一致，并与公众评分具有中高度相关性。同时，植物景观视觉效果受经济因素影响显著，并且街区外侧道路的视觉效果评分普遍高于内侧道路。可见，机器学习模型在历史街区植物景观视觉效果评价中具有较高的有效性，能够为历史街区植物景观的保护与优化提供科学依据，对城市规划和旅游业具有重要应用价值。

Previous studies on visual effects primarily focus on evaluating the overall urban environment, lacking specific research on historical districts within cities. In order to evaluate the visual effects of plantscapes in historic districts, street view images and machine learning methods were used. The ResNeSt model was selected to assess the coordination and health of plantscapes. The results show that the ResNeSt model performs best in classification and regression tasks. Its scores are consistent with expert evaluations and moderately to highly correlated with public evaluations. Additionally, the visual effects of plantscapes are significantly influenced by economic factors, with the visual effect scores of streets outside the historic districts generally higher than those inside. It is concluded that machine learning models are highly effective in evaluating the visual effects of plantscapes in historic districts. This provides a scientific basis for their protection and optimization, with important implications for urban planning and tourism.