LiDAR sensors have become an integral component in the realm of autonomous driving, widely utilized in environmental perception and vehicle navigation. However, in real-world road environments, contaminants such as dust and dirt can severely hamper the cleanliness of LiDAR optical windows, thereby degrading operational performance and affecting the overall environmental perception capabilities of intelligent driving systems. Consequently, maintaining the cleanliness of LiDAR optical windows is crucial for sustaining device performance. Unfortunately, the scarcity of publicly available LiDAR contamination datasets poses a challenge to the research and development of contamination identification algorithms. This paper first introduces a method for acquiring LiDAR-pollution datasets. LiDAR data acquisition on urban open roads simulates different types of pollution, including mud and leaves. The constructed dataset meticulously differentiates among the three states with clear labels: no pollution, mud pollution, and leaf pollution. In this paper, the PointNet algorithm is also optimised. The performance of the model is further improved by incorporating the reflection intensity into the model inputs. As a result, the model's accuracy on the test set exceeds 99% and meets the needs of real-time processing. This research not only contributes new ideas and methods for the development of LiDAR optical window contamination algorithms, but also establishes a foundation for future research and applications in LiDAR automatic cleaning.