[Objective] The study uses machine learning technology to analyze and obtain multi-labels for long social texts, aiming to provide new ideas for urban portrait text analysis and other related studies. It addresses the problems facing urban data portrait analysis, such as unstructured, different lengths, and non-singular topics in relevant analysis texts. [Methods] We retrieved social media texts on urban impressions from the Zhihu platform and performed sentence segmentation and noise reduction processing on the texts. Then, we manually annotated some texts using the existing urban portrait annotation framework. Next, we trained the support vector classification, convolutional neural networks, and Naive Bayesian and comprehensively evaluated their performance. We used the optimal model to obtain all labels for long texts, and utilized the ML-kNN multi-label learning model for training a multi-label social text classification model. [Results] Regarding the single-label text classification model, the support vector classification model had the best overall performance, with an accuracy rate of 0.690 0 for short text labeling. Using ML-kNN to build a multi-label text classification model, the highest accuracy rate reached 0.810 3, and the average Hamming loss was 0.035 3. [Limitations] The impact of textual context on topic classification needed to be fully considered. [Conclusions] Based on the long social text data on the Zhihu platform, the proposed multi-label classification model can effectively identify multiple labels for social long texts on the urban portrait.