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With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users’ learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning.

Recently, Chinese engineers have proposed a simple way to find the vehicles' critical speed, which is similar to the ramping method. In this article, through an example of vehicle of supercritical properties, it is proved that the new easier way is not scientifically justified and should not be used in engineering practice. In addition, the ramping way also yields inaccurate critical speed. Then one abnormal vibration phenomenon which appears on Beijing-Shanghai high-speed line is studied. The results demonstrate that it is car body hunting but not bogie hunting. Finally, through the computation and comparison of the lateral ride indices under different conditions, one stability problem about stochastic limit cycle banding is tentatively discussed.