UNet has been widely applied to image segmentation tasks due to its encoder⁃decoder structure and skip⁃connection mechanism. However, traditional downsampling operations (such as max pooling or average pooling) tend to cause loss of spatial detail information during feature compression, while the simple reconstruction approach of the upsampling stage struggles to fully model global contextual semantics. These limitations constrain the network's expressive power and segmentation accuracy. To address this, this paper proposes an enhanced UNet architecture by introducing the Interactive Pooling Module (IPM) and the Global⁃Local Feature Interaction Module (GLFIM) during subsampling and upscaling stages, respectively, to strengthen feature modeling capabilities.Results indicate that in the encoder, the IPM simultaneously integrates max⁃pooling and average⁃pooling branches. Through channel interaction and feature fusion, it effectively enhances feature representation capabilities while preserving more structural and statistical information. In the decoder, the proposed Local Block extracts local details through residual convolutions, while the Global Block combines an attention mechanism based on adaptive pooling with an MLP network to model long⁃range dependencies and global semantic context. Experimental results demonstrate that our method exhibits significant advantages in preserving critical details and enhancing global semantic understanding, achieving superior segmentation performance compared to the standard UNet model. On the WHU dataset, IoU and F1 scores reached 90.73% and 95.14%, respectively.

The combination of remote sensing image information extraction and artificial intelligence algorithms is an important technical tool for land use status survey, monitoring and management in land resources and environmental departments.Aiming at the problems of insufficient spatial information localization and inaccurate multi-scale target feature segmentation generated by U-net in remote sensing image extraction, a CA-Res2-Unet model incorporating an attention module into the head of Res2Net to replace the coding part of U-net is proposed, which aims to enhance the spatial localization and multi-scale feature information segmentation capability of U-net.Experiments were carried out on mainstream networks and improved models through the WHDLD public data set and the self-made data set of Shenfu New District. The results show that compared with the basic model, OA, M_IoU and F1 indexes of the experiment on the WHDLD public data set and the self-made data set of Shenfu New District increased by 0.92%, 2.00%, 1.58% and 1.18%, 2.87%, 1.91%, respectively. The visual effect and quantitative indexes of the proposed method are superior to other mainstream semantic segmentation networks, which can provide scientific basis for the investigation of the status quo of regional land use and the decision-making of relevant departments.

Aiming at the low efficiency of Apriori algorithm in scanning database and low dimensional frequent itemset, an efficient implementation method of Apriori algorithm was proposed, which is called EI_Apriori algorithm. This method utilizes the vector?based storage structure and pre?pruning to reduce the number of scanning databases and low?dimensional frequent itemsets and thus improves the efficiency of the Apriori algorithm. According to the actual situation of student achievement analysis, the constraints on the sequence relationship between courses are added in the association rule mining, and the constraints on the score level range are added in the association rules. The adjusted EI_Apriori algorithm was applied in score association analysis. The results show that the EI_Apriori algorithm can accurately find the association rules that meet the real needs, which proves the superiority of EI_Apriori algorithm.