For a long time, kindergarten literature reading course is often a mere formality, preschool children's reading invalid, random phenomenon. In order to improve preschool children's reading interest and reading comprehension ability, teachers should start from the core quality and deconstruct the characteristics of children's literature. Make use of multiple resources to optimize literary reading materials; Integrate contents in various fields and implement rich curriculum activities; Construct performance evaluation system and form reading evaluation model.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

Under the background of the development of the network information age, the current Internet industry has obtained more development opportunities, but it has also brought corresponding challenges in the process of wide application. In the development and construction of modernization, society pays more attention to the supervision and determination of the characteristics of online public opinion. From the perspective of the current characteristics of network public opinion, because social information is more extensive and involves many fields, network public opinion has a high degree of complexity and diffusion. Therefore, it is necessary to strengthen the analysis and application of relevant data mining systems in order to achieve efficient management of network public opinion. The key to the disadvantage of the traditional excavation of public opinion communication characteristics lies in the lag of the excavation process, and it is difficult to deal with malignant public opinion in a timely and effective manner. Therefore, in order to truly solve the lagging problem of public opinion data dissemination feature mining technology, it is necessary to strengthen the application of artificial intelligence technology in it.

With the globalization of social and economic development, the culture, economy, science and technology and materials of all countries in the world are communicating to varying degrees. The basic tool of communication is language. Therefore, language translation plays an important role in this process. Learning English translation is of great significance to the development and construction of our country. The establishment of English translation major in major universities is the base for cultivating English translation talents in our country. It is also the main place to improve students' English translation ability and practice. The text will focus on the existing problems and teaching practice application strategies of practical English in higher vocational education, so as to promote the development and use of English translation and improve the quality of teaching in our country.

Competency-based education is one of the many important educational objectives in the cultivation of senior vocational talents. In the past education model, the importance of achievement is greater than ability. Teachers rely on the scores of test papers to classify students' grades. Competency-based education has changed this situation very well, paying special attention to students' ability training. This paper mainly studies how to better promote the reform and innovation of English teaching in higher vocational colleges and strengthen students' learning ability and vocational skills while ensuring students' ability development.

Scientific inquiry activities are the process of children finding, analyzing and solving problems. Children's real inquiry begins with the search for answers to questions, which is actually the process of seeking answers to the questions they are interested in with direct perception, personal experience and practical operation. At the same time, in the process of children's SI, teachers should effectively use the interactive strategies of grasping the generation of questions, using questions to promote inquiry and using questions to revitalize inquiry, so as to support and promote children's in-depth learning and inquiry.