Academic Journal of Computing & Information Science, 2022, 5(14); doi: 10.25236/AJCIS.2022.051407.
Computer Simulation, Beijing, China
Due to the low F-Measure value of traditional methods in practical application, the classification effect of graphic office information is not good, so a classification method of graphic office information in intelligent office automation system is proposed. Through the semantic association of images and texts, the features of image and text office information are extracted, the weight of information features is determined, and the similarity between image and text office information is calculated according to the features, so as to achieve information classification. The experiment proves that the F-Measure value of the design method is high, and it has a good application prospect in the field of image and text office information classification.
Intelligent office automation system; Graphic office information; Classification; F-Measure value; Weight
Xiaoqian Zhang. Classification of graphic office information in intelligent office automation system. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 14: 44-47. https://doi.org/10.25236/AJCIS.2022.051407.
 Li Feng, Liu Lin, Fan Hongyan. Research on The Classification and Coding Technology of Information Model for Automobile Industrial Building Construction Based on OmniClass [J]. China Engineering Consulting,2021(11):94-98.
 Chen Xuanyi, Sun Xiaolei, Wu Zhenggang, et al. Research and Application of BIM Based Electromechanical Component Information Classification Coding Technology in Exhibition projects [J]. Installation, 2021(09):65-67.
 Zhang Li, Ma Jing. A text classification method combining word statistical characteristics and semantic information [J]. Computer Engineering & Science, 2021, 43(07):1308-1315.
 Wang Zhen, Zhang Haiqing, Peng Li, Et al.Information Extraction and Classification Method of Medical Data based on Singular Value Decomposition [J]. Journal of Chengdu University of Information Technology, 2020, 35(05):537-541.
 Zhang Dong-Dong, Peng Dun-Lu. Ent-Bert: Entity Relation Classification Model Combining BERT and Entity Information [J]. Journal of Chinese Computer Systems, 2020, 41(12):2557-2562.