Aiming at the problems that the existing knowledge graph recommendation models do not consider the periodic features of the user and the items to be recommended will affect the recent interests of the user， a knowledge graph recommendation model with Multiple Time scales and Feature Enhancement （MTFE） was proposed. Firstly， Long Short-Term Memory （LSTM） network was used to mine the user’s periodic features on different time scales and integrate them into user representation. Then， attention mechanism was used to mine the features strongly correlated with the user’s recent features in the items to be recommended and integrate them into the item representation after enhancement. Finally， the scoring function was used to calculate user’s ratings of items to be recommended. The proposed model was compared with PER（Personalized Entity Recommendation）， CKE（Collaborative Knowledge base Embedding）， LibFM， RippleNet， KGCN（Knowledge Graph Convolutional Network）， CKAN（Collaborative Knowledge-aware Attentive Network） knowledge graph recommendation models on real datasets Last.FM， MovieLens-1M and MovieLens-20M. Experimental results show that compared with the model with the best prediction performance， MTFE model has the F1 value improved by 0.78 percentage points， 1.63 percentage points and 1.92 percentage points and the Area Under Curve of ROC （AUC）metric improved by 3.94 percentage points， 2.73 percentage points and 1.15 percentage points on three datasets respectively. In summary， compared with comparative knowledge graph recommendation models， the proposed knowledge graph recommendation model has better recommendation effect.

Since Knowledge Graph（KG） can alleviate the problems of data sparsity and cold start in collaborative filtering algorithm， it has been widely studied and applied in the recommendation field. Many existing recommendation models based on KG confuse the collaborative filtering information in user-item bipartite graph and the association information between entities in KG， resulting in the learned user vector and item vector cannot accurately express the characteristics of users and items， and even introducing wrong information to interfere with recommendation. Regarding the issues above， a model called KG Attention Network fusing Collaborative Filtering information （KGANCF） was proposed. Firstly， the collaborative filtering information of users and items was dug out by the collaborative filtering layer of the network from the user-item bipartite graph， avoiding the interference of the entity information of KG. Then， the graph attention mechanism was applied in the KG attention embedding layer， the attribute information closely related to users and items was extracted from KG. Finally， the collaborative filtering information and the attribute information in KG were merged at the prediction layer to obtain the final vector representations of users and items， and then the scores of users to items were predicted. The experiments were carried out on MovieLens-20M and Last.FM datasets. Compared with the results of Collaborative Knowledge-aware Attentive Network （CKAN）， on Movielens-20M， F1-score of KGANCF improves by 1.1 percentage points while Area Under Curve （AUC） improves by 0.6 percentage points； on Last.FM， F1-score improves by 3.3 percentage points and AUC improves by 8.5 percentage points. Experimental results show that KGANCF can effectively improve the accuracy of recommendation results， and is significantly better than CKE （Collaborative Knowledge base Embedding），KGCN （Knowledge Graph Convolutional Network），KGAT （Knowledge Graph Attention Network） and CKAN models on datasets with sparse KG.

Current research on knowledge graph recommendation mainly focus on model establishment and training. However， in practical applications， it is necessary to update the model regularly by using incremental updating method to adapt to the changes of preferences of new and old users. Because most of these models only use the users’ long-term interest representations for recommendation， do not consider the users’ short-term interests， and during the aggregation of neighborhood entities to obtain the item vector representation， the interpretability of the aggregation methods is insufficient， and there is the problem of catastrophic forgetting in the process of updating the model， a Knowledge Graph Preference ATtention network based Long- and Short-term recommendation （KGPATLS） model and its updating method were proposed. Firstly， the aggregation method of preference attention network and the user representation method combining users’ long- and short-term interests were proposed through KGPATLS model. Then， in order to alleviate the catastrophic forgetting problem during model update， an incremental updating method Fusing Predict Sampling and Knowledge Distillation （FPSKD） was proposed. The proposed model and incremental updating method were tested on MovieLens-1M and Last.FM datasets. Compared with the optimal baseline model Knowledge Graph Convolutional Network （KGCN）， KGPATLS has the Area Under Curve （AUC） increased by 2.2% and 1.4% respectively and the Accuracy （Acc） increased by 2.5% and 2.9% on the two datasets respectively. Compared with three baseline incremental updating methods on the two datasets， the AUC and Acc indexes of FPSKD are better than those of Fine Tune and Random Sampling respectively， the training time index of FPSKD is reduced to about one eighth and one quarter of that of Full Batch respectively. Experimental results verify the performance of KGPATLS model and that FPSKD can update the model efficiently while maintaining the model performance.

In order to solve the difficulty of feature extraction and low accuracy of retrieval in pulmonary nodule image retrieval， a deep network model named LMSCRnet was proposed to extract image features. Firstly， the feature fusion method of convolution of filters with different scales was adopted to solve the problem of difficulty in obtaining local features caused by different sizes of pulmonary nodules. Then， the SE-ReSNeXt block was introduced to obtain the semantic features with higher level and reduce network degradation. Finally， the high-level semantic feature representation of pulmonary nodule image was obtained. In order to meet the needs of massive data retrieval tasks in real life， the distance calculation and sorting process were deployed on the Spark distributed platform. The experimental results show that the feature extraction method based on LMSCRnet can better extract the image high-level semantic information， and can achieve 84.48% accuracy on the preprocessed dataset of lung nodules named LIDC， and has the retrieval precision higher than other retrieval methods. At the same time， using Spark distributed platform to complete similarity matching and sorting process enables the retrieval method to meet the requirements of massive data retrieval tasks.