Abstract: The present disclosure discloses a text classification system based on feature selection and a method thereof in the technical field of natural language processing and short text classification, comprising: acquiring a text classification data set; dividing the text classification data set into a training text set and a test text set, and then pre-processing the training text set and the test text set; extracting feature entries from the pre-processed training text set through improved chi-square statistics to form feature subsets; using TF-IWF algorithm to give the weight to the extracted feature entries; based on the weighted feature entries, establishing a short text classification model based on a support vector machine; and classifying the pre-processed test text set by the short text classification model. The present disclosure solves the problem that the short text content is sparse to some extent, thereby improving the performance of short text classification.

Abstract: A method for processing a mortise of a small-size superalloy turbine disk using laser shock peening A laser shock peening (LSP) parameter of a laser shock peening (LSP) robot is designed. A spatial data of a motion trajectory of the LSP robot for a laser shock peening area of mortise teeth of the first mortise is found and recorded using a robot simulation system software to generate a trajectory program of the robot for the first mortise. Then a rotation program of an end of an arm of the LSP robot is written according to an included angle between two adjacent mortises to generate a trajectory program of the robot for all mortises of the turbine disk. Finally, the turbine disk is processed by laser shock peening.