Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). Firstly, discrete wavelet transform (DWT) is used to denoise the data, then an autoencoder (AE) technology is used to extract the data features, and finally, bidirectional long short-term memory (BiLSTM) is used for prediction. To verify the effectiveness of the proposed DWT_AE_BiLSTM model, we studied three different power stations and compared their performance with the shallow neural network model. Experimental analysis shows that this model is more competitive in forecasting accuracy and stability. Compared with the BP model, the proposed model has increased by 3.86%, 3.22% and 3.42% in three wind farms, respectively.

Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience. To address the limitations of existing studies in multimodal heterogeneous data fusion and high-order relationship modeling, this research proposes a Heterogeneous Hypergraph and Attention-based Online Course Recommendation (HHAOCR) algorithm. By constructing a heterogeneous hypergraph structure encompassing three entity types (students, instructors, and courses), we innovatively designed hypergraph convolution operators to achieve bidirectional vertex-hyperedge information aggregation, integrated with a dynamic attention mechanism to quantify important differences among entities. The method establishes computational frameworks for hyperedge-vertex coefficient matrices and inter-hyperedge attention scores, effectively capturing high-order nonlinear correlations within multimodal heterogeneous data, while employing temporal attention units to track the evolution of user preferences. Experimental results on the MOOCCube dataset demonstrate that the proposed algorithm achieves significant improvements in NDCG@15 and F1-Score@15 metrics compared to TP-GNN (enhanced by 0.0699 and 0.0907) and IRS-GCNet (enhanced by 0.0808 and 0.0999). This work provides a scalable solution for multisource heterogeneous data fusion and precise recommendation for online education platforms.

Now, there are more and more hot event of network. The event itself is complex, and have too much useless information. In this paper, we propose a formal method which uses semantic fingerprint to represent the hot event of network, and it can be very intuitive expression important information of hot events. Based on the graph theory, by the event title, the text sentence as the node, through the interaction between them, get hot events on behalf of the sentence. Then use the TF-IDF method to get the feature word from the sentence expression the semantic fingerprints of hot events. The semantic fingerprint is applied to the retrieval model to prove the validity of the event semantic fingerprint method.

It's necessary to acquire semantic knowledge in Natural Language Processing research. In this paper, we present an approach for acquiring Chinese semantic knowledge based on maximum entropy model. Semantic knowledge units are composed of central word and a group of feature words. Because the maximum entropy to extract features, we utilize it to calculate the semantic distance between the central word and feature words in large-scale network corpus. In the experiment, tests on a number of manual defined data sets show that the Spearman correlation coefficient has been increased 6.2%-20.9%.