**6. Conclusions**

Inspired by the idea of YOLO and other object detection convolutional neural networks, this paper cuts the remote sensing image, analyzes the haze concentration in different areas qualitatively and quantitatively, and derives the spatial laws of different seasons to predict and analyze the haze in a finer time dimension. This paper first proposes the structure of the multi-convolution joint neural network, classifies the spatio-temporal data of haze in the Beijing area by block level, and carries out the frequency statistical analysis method on the output results of the multi-convolution joint neural network. First, the frequency of occurrence of haze levels was displayed and analyzed accordingly. Then, to analyze the results more finely, the Moran's *I* of spatial autocorrelation analysis was used in subsequent research to analyze the spatial relationship between each block. Then, to analyze the haze's temporal and spatial evolution more intuitively, the spatial variation of the haze is analyzed in the LISA cluster map on the time unit of the seasonal scale. Finally, it is concluded that the temporal and spatial distribution of haze in the Beijing area is high in the south and low in the north. Moreover, its temporal and spatial evolution characteristics on a seasonal scale are that, according to the time changes from winter, spring, summer to autumn, the relationship of the haze concentration between each sub-region gradually changes from a discrete state to a concentrated state.

**Author Contributions:** Conceptualization, W.Z. and L.W.; methodology, L.Y. and S.L.; software, W.H.; formal analysis, B.Y. and L.W.; data curation, W.H.; writing—original draft preparation, L.Y. and W.H.; writing—review and editing, L.Y. and W.Z.; funding acquisition, W.Z. and B.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** Thisworkwas jointly supported by the Sichuan Science and Technology Program (Grant:2021YFQ0003, 2019YJ0189).

**Institutional Review Board Statement:** Not Applicable.

**Informed Consent Statement:** Not Appliable.

**Data Availability Statement:** The haze level data used in this paper is an open-resource data provided by UCI Center for Machine Learning and Intelligent System at https://archive.ics.uci.edu/ ml/datasets/Beijing+Multi-Site+Air-Quality+Data. The MODIS data is also an open-resource data provided by NASA at https://modis.gsfc.nasa.gov/data/.

**Conflicts of Interest:** The authors declare no conflict of interest.
