In recent years， more and more schools tend to use online education widely. However， learners are hard to search for their needs from the massive learning resources in the Internet. Therefore， it is very important to research the online learning resource recommendation and perform personalized recommendations for learners， so as to help learners obtain the high-quality learning resources they need quickly. The research status of online learning resource recommendation was analyzed and summarized from the following five aspects. Firstly， the current work of domestic and international online education platforms in learning resource recommendation was summed up. Secondly， four types of algorithms were analyzed and discussed： using knowledge point exercises， learning paths， learning videos and learning courses as learning resource recommendation targets respectively. Thirdly， from the perspectives of learners and learning resources， using the specific algorithms as examples， three learning resource recommendation algorithms based on learners’ portraits， learners’ behaviors and learning resource ontologies were introduced in detail respectively. Moreover， the public online learning resource datasets were listed. Finally， the current challenges and future research directions were analyzed.

Clustering is a technique to find the internal structure between data， which is a basic problem in many data-driven applications. Clustering performance depends largely on the quality of data representation. In recent years， deep learning is widely used in clustering tasks due to its powerful feature extraction ability， in order to learn better feature representation and improve clustering performance significantly. Firstly， the traditional clustering tasks were introduced. Then， the representative clustering methods based on deep learning were introduced according to the network structure， the existing problems were pointed out， and the applications of deep learning based clustering in different fields were presented. At last， the development of deep learning based clustering was summarized and prospected.

In order to solve the problem that the algorithms of publication venue recommendation always consider the text topics or the author’s history of publications separately， which leads to the low accuracy of publication venue recommendation results， a contribution recommendation algorithm of academic journal based on author preferences was proposed. In this algorithm， not only the text topics and the author’s history of publications were used together， but also the potential relationship between the academic focuses of publication venues and time were explored. Firstly， the Latent Dirichlet Allocation （LDA） topic model was used to extract the topic information of the paper title. Then， the topic-journal and time-journal model diagrams were established， and the Large-scale Information Network Embedding （LINE） model was used to learn the embedding of graph nodes. Finally， the author’s subject preferences and history of publication records were fused to calculate the journal composite scores， and the publication venue recommendation for author to contribute was realized. Experimental results on two public datasets， DBLP and PubMed， show that the proposed algorithm has better recall under different list lengths of recommended publication venues compared to six algorithms such as Singular Value Decomposition （SVD）， DeepWalk and Non-negative Matrix Factorization （NMF）. The proposed algorithm maintains high accuracy while requiring less information from papers and knowledge bases， and can effectively improve the robustness of publication venue recommendation algorithm.