城市街区功能的识别是城市规划与管理的重要基础，随着城市化进程的加快，单一用途的划分已难以满足复杂城市空间的需求。作为城市多功能融合的体现，混合功能街区的识别，尤其是自动识别，对理解城市功能多样性与提升土地利用效率具有重要意义。在此背景下，本文以开源兴趣面AOI和兴趣点POI蕴含的功能标签为基础，联合开放地图OSM与哨兵二号影像提出了一种能够自动提取纯净和混合功能样本的方法，进而利用ResNet34模型实现街区的具体功能识别。首先利用POI分布信息熵区分单一与混合用途的街区，然后基于哨兵二号与单一功能的用地样本设计多视图差异学习模块进一步提取单一和混合类的样本。此外，考虑到AOI范围与真实街区的尺度差异，样本自动提取方案分别应用于AOI和街区两种单元以增加样本的数量和尺度多样性。本文提出的自动分类方法在北京、合肥、潍坊和成都四个城市的总体精度分别为72.9%、78.3%%、73.4%和75.1%，与仅使用POI分布信息熵的方法相比，联合AOI和POI的方式将混合类别的识别精度分别提高了7%、18%、20%和13%。这一结果证明了该方法在不同城市环境下的可行性与有效性，以及众源地理数据和遥感影像的结合在城市街区用途，特别是混合用途识别研究中的潜力。

The identification of the functions of urban blocks serves as a crucial foundation for urban planning and management. With the acceleration of urbanization, the division of single-functional zones can no longer adequately meet the demands of complex urban spaces. As a manifestation of multifunctional urban integration, the identification of mixed-function blocks—particularly automated identification—holds significant importance for understanding urban functional diversity and enhancing land use efficiency. Against this backdrop, this study proposes an automated sample extraction method for seven categories, including pure and mixed-use zones, by integrating single functional information derived from Area of Interest (AOI) and Point of Interest (POI) data, along with OpenStreetMap (OSM) and Sentinel-2 imagery. The ResNet34 model is then employed to achieve functional identification at the neighborhood level. First, the information entropy of POI distribution is utilized to distinguish between single functional and mixed-use neighborhoods. Subsequently, a multi-view discrepancy learning module, based on Sentinel-2 imagery and single functional samples, is designed to further extract samples for both single and mixed categories. Considering the scale discrepancy between AOIs and actual urban blocks, the automated sample extraction scheme is applied to both AOI and neighborhood units to enhance sample quantity and scale diversity. The proposed automatic classification method in this study achieved overall accuracies (OA) of 72.9%, 78.3%, 73.4%, and 75.1% in Beijing, Hefei, Weifang, and Chengdu, respectively. Compared to the approach using solely POI distribution entropy, the combined use of AOI and POI data improved the recognition accuracy for mixed-function categories by 7%, 18%, 20%, and 13% in these four cities. These results demonstrate the feasibility and effectiveness of the method across diverse urban environments, as well as the potential of integrating crowdsourced geographic data and remote sensing imagery in urban functional zone studies—particularly in the context of mixed use urban functional zones.