Abstract. In this paper, we propose a novel intelligent classification model to classify the railway signal equipment fault based on SMOTE and ensemble learning. To tackle the imbalanced fault text data, the model uses SMOTE algorithm to generate the minority railway signal equipment fault class data randomly, making the data balanced. Then the model adopts the base classifier, such as Logistic Regression, Multinomial Naive Bayes, SVM and the ensemble classifier, such as GBDT, Random Forests to classify the data processed by SMOTE. To combine the advantages of various classifiers, the model integrates multiple classifiers by way of voting. Based on the experiment analysis of railway signal equipment fault text data from 2012 to 2016, the result shows that the model has a significant improvement in fault classification accuracy, recall rate and f-score.

In combination with the characteristics of adult education, this paper presents the design and implementation of an virtual practical platform for adult education based on internet. The platform includes preparation, operation, test and community modules. It is valuable to those who intend to develop adult education, advocate lifelong learning, but have no space and financial resources to build practice base.

With the advent of the big data era, the application of big data technology in the railway industry is becoming more and more widespread. Through the analysis and application of big data, the management level and economic benefits of the China Railway Corporation can be effectively improved. In order to achieve a good analysis and application effect, it is necessary to unify the basic data in each business system of the railway. As the core data that can support the basic data of railway big data applications, railway master data plays an important role in the big data application of railway. Therefore, this paper proposes the overall framework of railway master data management from the perspective of big data applications. And it studies key technologies of railway master data such as data modeling, data cleaning, maintenance procedures and version management. Finally, it introduces the application of master data standard in big data integration. And this is of great significance to the extensive application of big data technology in the railway industry.

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Abstract. In this paper, to guarantee that the train can take measures to reduce the damage caused by the earthquake, it propose an irregular GI S curve fitting based high-speed railway earthquake influence range calculation model. Firstly, this model eliminates the abnormal points, calculates feature points and finds demarcation points of the highspeed railway GI S curve to get the processed point collection in Mercator coordinate. Secondly, though usin g the processed point collection, this model applies least square polynomial segmentation fitting method to implement complex high-speed GI S curve fitting. Thirdly, calculate the earthquake influence rang on high-seed railway line, according to the scope of the earthquake equation and the high -speed railway GI S curve fitt ed equation. Finally, the paper selects the Beijing So uth to Dezhou East high-speed railway section which is part of Beijing-Shanghai line a s a case study, which proves that the model can calculate the earthquake influence scope on the railway line offering decision support for train operation to ensure safety.