The intelligent education recommendation system can recommend knowledge suitable for students' personal learning. However, the traditional recommendation algorithm has generality problems, which lead to poor knowledge recommendation effects. In order to improve the performance of the education recommendation system, based on the machine learning algorithm, this paper combines the knowledge graph algorithm to improve the recommendation algorithm and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation. Moreover, this paper conducts in-depth mining of the potential attributes of users and items and improves the matrix decomposition formula based on knowledge recommendation requirements. In addition, this paper constructs the framework of the intelligent education recommendation system with IoT networks based on the analysis of functional requirements. Finally, this paper designs experiments to verify and analyze the model from the perspective of model performance and user satisfaction. The research results show that the algorithm model constructed in this paper is effective.

Unstructured textual news data is produced every day; analyzing them using an abstractive summarization algorithm provides advanced analytics to decision-makers. Deep learning network with copy mechanism is finding increasing use in abstractive summarization, because copy mechanism allows sequence-to-sequence models to choose words from the input and put them directly into the output. However, since there is no explicit delimiter in Chinese sentences, most existing models for Chinese abstractive summarization can only perform character copy, resulting in inefficiency. To solve this problem, we propose a lexicon-constrained copying network that models multigranularity in both encoder and decoder. On the source side, words and characters are aggregated into the same input memory using a Transformer-based encoder. On the target side, the decoder can copy either a character or a multicharacter word at each time step, and the decoding process is guided by a word-enhanced search algorithm which facilitates the parallel computation and encourages the model to copy more words. Moreover, we adopt a word selector to integrate keyword information. Experiment results on a Chinese social media dataset show that our model can work standalone or with the word selector. Both forms can outperform previous character-based models and achieve competitive performances.

The modern stage focuses more on structural changes, and the numerical control technology realizes the complex changes of the stage scenes and the precise movement of stage props. The study object in this article is musical drama, and it uses digital technologies to digitally manipulate the soundtrack. This study offers a low-complexity feature space minimal variance algorithm that combines the power approach to address the concerns of inadequate resolution improvement, excessive complexity, poor real-time performance, and low resilience of standard MV methods. The method has a high resolution, low complexity, and great resilience, and it may be utilized in a variety of stages. In addition, this paper combines digital technology to process music, enhances the promotion of music in musical performances, and allows performers to integrate in more effectively. Finally, through experimental research, it can be known that the music digital processing technology proposed in this paper can play a good role in promoting musical performances.

Based on the importance of climate change in the environmental and economic fields, this study analyzes the impact of climate change on the economy based on the current situation, including the impact on sensitive industries and the impact on each district, in order to study the impact of abnormal atmospheric anticyclones on the economy. Furthermore, this study enhances the image-tracking algorithm, fuses the registered radar picture using the wavelet multiresolution analysis approach, and decomposes the registered radar image using the Mallat algorithm to retrieve the low- and high-frequency components of the image. Because the information included in the frequency components after wavelet decomposition differs, this study uses appropriate fusion algorithms for the decomposed low-frequency and high-frequency components, respectively, to produce panoramic photographs with acceptable visual effects. Through experimental research, it can be seen that the statistical analysis model of anomalous anticyclone in ocean atmosphere and economy based on the target tracking algorithm proposed in this study has a good simulation operation effect, and it also verifies that there is a certain correlation between the atmospheric anomalous anticyclone and the economy.