To address the problem that relevant models typically only model characters and relevant vocabulary without fully utilizing the unique glyph structure information and entity type information of Chinese characters， a model that integrates prior knowledge and glyph features for Named Entity Recognition （NER） task was proposed. Firstly， the input sequence was encoded using a Transformer combined with Gaussian attention mechanism， and the Chinese definitions of entity types were obtained from Chinese Wikipedia. Bidirectional Gated Recurrent Unit （BiGRU） was used to encode the entity type information as prior knowledge， which was combined with the character representation using an attention mechanism. Secondly， Bidirectional Long Short-Term Memory （BiLSTM） network was used to encode the long-distance dependency relationship of the input sequence， and a glyph encoding table was used to obtain traditional Chinese characters’ Cangjie codes and simplified Chinese characters’ modern Wubi codes. Then， Convolutional Neural Network （CNN） was used to extract glyph feature representations， and the traditional and simplified glyph feature representations were combined with different weights， which were then combined with the character representation encoded by BiLSTM using a gating mechanism. Finally， decoding was performed using Conditional Random Field （CRF） to obtain a sequence of named entity annotations. Experiment results on the colloquial dataset Weibo， the small dataset Boson， and the large dataset PeopleDaily show that， compared with the baseline model MECT （Multi-metadata Embedding based Cross-Transformer）， the proposed model has the F1 value increased by 2.47， 1.20， and 0.98 percentage points， respectively， proving the effectiveness of the proposed model.

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.

Aiming at the problems of the slow model convergence and low diagnosis accuracy due to the time-series fault diagnosis data with strong noise in the industrial field， an improved one-Dimensional Convolutional and Bidirectional Long Short-Term Memory（1DCNN-BiLSTM） Neural Network fault diagnosis method was proposed. The method includes preprocessing of fault vibration signals， automatic feature extraction and vibration signal classification. Firstly， Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） technology was used to preprocess the original vibration signal. Secondly， the 1DCNN-BiLSTM dual channel model was constructed， and the processed signal was input into the Bidirectional Long Short-Term Memory （BiLSTM） model channel and the One-dimensional Convolution Neural Network （1DCNN） model channel to fully extract the timing correlation characteristics， the non-correlation characteristics of the local space and the weak periodic laws of the signal. Thirdly， in response to the problem of strong noise in the signal， the Squeeze and Excitation Network （SENet） module was improved and applied to the two different channels. Finally， the features extracted from the two channels were fused by putting them into the fully connected layer， and the accurate identification of equipment faults was realized by the help of the Softmax classifier. The bearing dataset of Case Western Reserve University was used for experimental comparison and verification. The results show that after applying the improved SENet module to the 1DCNN channel and the stacked BiLSTM channel at the same time， the 1DCNN-BiLSTM dual channel model performs the highest diagnosis accuracy 96.87% with fast convergence， which is better than traditional one-channel models， thereby effectively improving the efficiency of equipment fault diagnosis.

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.