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With the explosive growth in Internet news media and the disorganized status of news texts, this paper puts forward an automatic classification model for news based on a Convolutional Neural Network (CNN). In the model, Word2vec is firstly merged with Latent Dirichlet Allocation (LDA) to generate an effective text feature representation. Then when an attention mechanism is combined with the proposed model, higher attention probability values are given to key features to achieve an accurate judgment. The results show that the precision rate, the recall rate and the F1 value of the model in this paper reach 96.4%, 95.9% and 96.2% respectively, which indicates that the improved CNN, through a unique framework, can extract deep semantic features of the text and provide a strong support for establishing an efficient and accurate news text classification model.

Individual knowledge management is basic component of organizational knowledge management, and the maturity of individual knowledge management has a significant impact on organizational knowledge management. This research introduces scientific idea of capability maturity model into individual knowledge management, building corresponding assessment criteria combined with features of individual knowledge management, and constructing an individual knowledge management maturity model with gray comprehensive evaluation method. In the fourth part of this paper, the validity of the model has been verified by applying the model on an instance. This research is made in order to provide references and suggestions on improving the level of individual knowledge management in knowledge-based organizations.