The existing parking lot classification methods are exposed to problems of low-level automation and high equipment and deployment costs, and the existing detection algorithms have low recall rates and poor detection accuracy. To solve these problems, this study proposes a vision-based parking space detection and classification algorithm to improve the utilization efficiency of parking lots. First, parking spaces are detected to help build a parking space table andincrementally expand the parking space classification model dataset. Then, the test dataset is used to train the support vector machine (SVM) model for parking space classification. Finally, real-time judgment of the parking space conditions is made on every parking space based on the surveillance video data. The experimental results show that under different lighting conditions, the recall rate of the line detection of parking spaces is above 94%, and the accuracy of the parking space classification model is above 95%. The algorithm boasts a high degree of automation, good accuracy, simple deployment, and high application value.