The introduction of multimodal information to enhance knowledge graph link prediction has become a recent hotspot. However， most existing methods typically rely on simple concatenation or attention mechanisms for multimodal feature fusion， ignoring the correlation and semantic inconsistency between different modalities， which may fail to preserve modality-specific information and inadequately exploit the complementary information between modalities. To address these issues， a multimodal knowledge graph link prediction model based on cross-modal attention mechanism and contrastive learning was proposed， namely FITILP（Fusing Image and Textual Information for Link Prediction）. Firstly， pretrained models， such as BERT （Bidirectional Encoder Representation of Transformer） and ResNet （Residual Network）， were used to extract textual and visual features of entities. Then， a Contrastive Learning （CL） approach was applied to reduce semantic inconsistencies across modalities. A cross-modal attention module was designed to refine text feature attention parameters using image features， thereby enhancing the cross-modal correlations between text and image features. And Translation models， such as TransE （Translating Embeddings） and TransH （Translation on Hyperplanes）， were employed to generate graph structural， visual， and textual features. Finally， the three types of features were fused to perform link prediction between entities. Experimental results on the DB15K dataset show that the FITILP model improves Mean Reciprocal Rank （MRR） by 6.6 percentage points compared to single-modal baseline TransE， and achieves improvements of 3.95， 11.37， and 14.01 percentage points in Hits@1， Hits@10 and Hits@100， respectively. The results indicate that the proposed method outperforms comparative baseline methods， demonstrating its effectiveness in leveraging multimodal information to enhance prediction performance.

To address the problems of poor targeted performance， unclear classification and lack of datasets faced by agricultural news， an agricultural news classification model based on Enhanced Representation through kNowledge IntEgration （ERNIE）， Deep Pyramidal Convolutional Neural Network （DPCNN） and Bidirectional Gated Recurrent Unit （BiGRU）， called EGC， was proposed. The dataset was first encoded by using ERNIE， then the features of the news text were extracted simultaneously by using the improved DPCNN and BiGRU， and the features extracted were combined and the final results were obtained by Softmax. To make EGC model more suitable for applications in the field of agricultural news classification， the DPCNN was improved by reducing its convolution layers to preserve more features. Experimental results show that compared with ERNIE， the precision， recall and F1 score of the proposed EGC model are improved by 1.47， 1.29 and 1.42 percentage points， respectively， verifying that EGC is better than traditional classification models.

The memory data will change after occurring the attack behaviors， and benchmark measurement used by the traditional integrity measurement system has the problems of low detection rate and lack of flexibility. Aiming at the above problems， a memory combined feature classification method based on multiple Back Propagation （BP） neural networks was proposed. Firstly， the feature value of the memory data was extracted by Measuring Object Extraction Algorithm （MOEA）. Then， the model was trained by different BP neural networks. Finally， a BP neural network was used to collect the obtained data and calculate the safety status score of the operating system. Experimental results show that compared with the traditional integrity measurement system using benchmark measurement， the proposed method has much higher accuracy and universality， and the proposed method has a detection accuracy of 98.25%， which is higher than those of Convolutional Neural Network （CNN）， K-Nearest Neighbor （KNN） algorithm and single BP neural network， verifying the proposed method can detect attack behaviors more accurately. The proposed method has the model training time about 1/3 of the traditional single BP neural network， and also has the model training speed improved compared with similar models.