In view of the fact that today's railway engineering staff cannot be real-time monitored during operation, and safety incidents occur from time to time, seven main behaviors of them are analyzed by taking railway inspectors as an example. An embedded device integrated with an accelerometer is worn by every worker, collects their behavior data and extracts features, and uses four kinds of classifiers, which are C4.5 decision tree, random forest, KNN, and SVM, to carry out experiments. The results show that classifier SVM performs the best, the behavioral recognition accuracy rate reaches 99.2%. This research has certain engineering application value for eliminating the safety hazards of railway field engineering staffs.