With the significant increase in the number and frequency of private cars in cities, the problem of "parking difficulties" has gradually become a "bottleneck" that restricts urban development. In order to make reasonable use of the city's limited parking resources, the best way is to establish a city-level parking guidance system. At this stage, there is no effective solution. The reason is that the cost of obtaining parking data is too high. Therefore, how to reduce the procurement cost without affecting the accuracy of the parking data becomes the key to solving the problem of "parking difficulties". First, based on the spatiotemporal sensitivity of parking data, parking lots with significant data differences are divided into different clusters. After verifying that parking data in the same cluster complies with Pareto's principle, the top 20% of the most influential parking lots are selected as sample parking. At the site, sensors are installed to obtain real-time parking data and used as sample data. Considering that the patch data obtained by the existing algorithm is not satisfactory, this study upgrades the one-dimensional parking data to two-dimensional, and uses the improved Deep Convolution Generative Adversarial Networks (DCGAN) to generate new data settings and sample data. Roughly the same, any new data set can be used as any missing parking data in the same cluster. The implementation results show that the proposed scheme in this study can not only obtain a large number of high-quality "pseudo-data" in batches under the condition of limited perception, greatly reduce the acquisition cost of parking data greatly, but also significantly improve the repair effect compared with the current research.