Multi-feature fusion-based online academic report classification method

Abstract

The invention discloses a multi-feature fusion-based online academic report classification method. The method is characterized by comprising the steps of (1) collecting and building an academic report database and coarsely classifying an academic report into certain subjects homogenous with an organizer according to organizer information of the academic report in the database; (2) building a database of researchers in various fields around the country, matching the database with reporter information in the academic report and determining classification; (3) extracting a keyword in a data header of the academic report; (4) carrying out synonym feature expansion on the extracted keyword; (5) carrying out text classification on obtained features after synonym expansion; and (6) synthesizing the classification result to obtain the final classification result of the academic report. The academic report is classified through a multi-factor method, so that the classification speed and accuracy are ensured.

Description

technical field [0001] The invention belongs to the technical field of text classification based on machine learning, and mainly relates to a method for classifying academic report forecasts based on multi-feature fusion. Background technique [0002] Academic reports help to broaden horizons and knowledge and obtain cutting-edge research information. The content of academic reports is usually the latest research results of the reporter, which helps scientific and technological workers understand the latest research progress of the discipline, and can also obtain cross-disciplinary knowledge through this way. Subject knowledge, through face-to-face listening, can also acquire the knowledge and thinking of experts, which helps scientific and technological workers to unlock their inherent intuitive ability, and can also use the environment to unlock their inherent intuitive ability. [0003] In addition, as the number of scientific and technological workers continues to increa...

AI Technical Summary

Problems solved by technology

[0008] The method of manual classification is very simple, but it is only suitable when the amount of data is extremely small. Manual classification requires a lot of manpower and energy, and requires the participation of professional personnel, otherwise the accuracy rate will not be very high, and the method is not practical.

Classification using machine learning methods is suitable for those situations where the text does not contain helpful information for classification. Currently, there are many methods for text classification using machine learning, but they still cannot achieve high accuracy.

Integrating various methods of machine learning and adding other useful features contained in the text for fusion classification, but still unable to meet the actual classification accuracy requirements

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