As a broad information filtering strategy, customized recommendation systems study the historical conversation information between users and items to develop individual interest designs in an environment of “information overload”, allowing people to uncover and recommend information that interests them. Nonetheless, the explosive development of information when you look at the External fungal otitis media system makes people walk within the ocean of information, and it is progressively difficult to get the knowledge they absolutely need, i.e., information overburden. This has offered increase to customized suggestion systems, which actually have older applications in industries such as e-commerce, music services, and movie services. To the end, this paper studies and implements a customized educational resource recommendation system that will manage big data. The results reveal that the values of various similarity calculations all fluctuate using the gradual enhance associated with range nearest neighbors, in addition to algorithm in this paper is optimum at the number of next-door neighbors around 60; then, it’s inferred that using the calculation method to the recommendation algorithm will improve suggestion reliability. Consequently, education uses the idea of big data to process the huge level of training data and locate some correlations and rules in training, in order to realize “teaching in accordance with the material, teaching in accordance with the material”.With the continuous improvement and improvement the socialist marketplace economic climate, China’s economic development has actually complete energy, however the domestic marketplace is not enough to meet up with the requirements of enterprise development. China features constantly TPX-0005 molecular weight centered on calm diplomacy, plus the globe marketplace has actually a solid need for Chinese services and products. This work aims to improve reliability of trade rate forecasting. The danger elements that may be encountered in the financial investment procedure of international enterprises can be Shell biochemistry efficiently averted. Incorporating the benefits of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), the LSTM-CNN (Long Short-Term Memory-Convolutional Neural Network) model is suggested to anticipate the volatility trend of stocks. Firstly, the investment risk of international businesses is reviewed, and, secondly, the concepts associated with the utilized CNN and LSTM are expounded. Finally, the overall performance of this suggested model is validated by setting experiments. The experimental results show that whenever forecasting the 10 chosen stocks, the suggested LSTM-CNN model gets the greatest precision in predicting the volatility of shares, with an average accuracy of 60.1%, as the normal precision for the remaining portion of the models is perhaps all below 60%. It may be unearthed that the stock group does not have an excellent impact on the prediction precision regarding the design. The average prediction accuracy associated with the CNN design is 0.578, that is less than that of the Convolutional Neural Network-Relevance design, while the forecast precision regarding the LSTM design is 0.592, that is much better than compared to the Long Short-Term Memory-Relevance design. The created design can help anticipate the stock exchange to guide people to produce efficient assets and reduce investment dangers considering appropriate instances. The investigation tends to make a certain contribution to improving the company’s earnings and stabilizing the national financial development.The function would be to study using mathematical evaluation in monetary technology (FinTech) development within the age of digital economy. An Evaluation Index System (EIS) when it comes to present scenario of Chinese FinTech companies is set up by thinking about the effect associated with the era regarding the digital economy from the growth of FinTech. Particularly, the Principal Component Analysis (PCA) is introduced to construct the principal component prediction model according to useful information. Then, six Chinese State-owned Enterprises (SOEs) tend to be selected. Their stock costs are predicted with the recommended model through an empirical study. The results show that picking three main elements to guage the monetary circumstances of six SOEs is reasonable. The accumulated difference values associated with the first three major aspects of the stock’s closing cost and orifice cost are all more than 85%. Therefore, the chosen three major components can acquire the potential information associated with initial data.