Street View Imagery (SVI) is one of the important data sources for the quantitative research of urban built environment. However, it is difficult to fully represent all the information with one type of feature in the SVI due to its complexity and diversity. In this paper, we proposed an effective multi-feature fusion method to evaluate the street space quality based on SVI. Taking Yuexiu district in Guangzhou city as the study area, the Baidu SVIs in the four orientations (front, behind, left, right) at the sample points were obtained. Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradient (HOG) were derived from SVIs as handcrafted features. Semantic features were also derived from SVIs using ENet convolution neural network as features based on deep learning. Based on single feature and multi-feature fusion, Support Vector Machine (SVM) and Random Forest (RF) were used to train the street space quality evaluation model for the four orientations on the training set. The optimal model and the combination of features were selected according to the classification accuracy and Kappa coefficient on the test set. Results showed that: (1) The optimal classification accuracy of models based on SVM was 82.8% (front), 81.7% (behind), 76.6% (left), 76.6% (right), respectively. In the models based on single feature, the average accuracy of the models based on HOG feature (73.03%) or semantic feature (72.28%) was better than the SURF feature (56.00%). The optimal classification accuracy of the models based on RF algorithm was 82.8% (front), 85.0% (behind), 78.1% (left), 70.3% (right). (2) The accuracy of front and behind orientation model was slightly better than that of left and right orientation. The optimal models of each orientation all are multi-feature fusion models. The average classification accuracy and Kappa coefficient of these optimal evaluation models was 80.6% and 0.62, respectively. These results showed that the proposed method could achieve a high recognition performance. (3) The selection and fusion of features were more determined to the model performance when the SVI were used to evaluate the street space quality, while the performance difference between the two algorithms was small. (4) There were obvious spatial differences in the street space quality of Yuexiu district. The street space quality in the southeast of Yuexiu district needed to be improved. A large scale and high precision street space quality evaluation method was proposed based on multi-feature fusion of SVI and achieved a high recognition performance in this study. And the street space quality score in Yuexiu district was obtained. These results could provide valuable clues for local authorities to conduct comprehensive renovations of urban built environment.