Abstract: In recent years,the rise of artificial intelligence has promoted the development of green environ mental protection and intelligent production in China coal industry.To solve the problems of slow interpretation,long period,and high misjudgment rate in the recognition of aromatic lattice fringes,an intelligent extraction method based on MASK R CNN was proposed.The intelligent extraction method improves the recognition algorithm of lattice fringes by adjusting the feature parameters and optimizing the network structure:① skipping the prepare process,the extraction training of aromatic lattice fringes in end to end mode was performed and the function of automatically identifying aromatic lattice fringes from the original HRTEM was realized;and ② Breaking through the mental shackles of conventional neural networks to extract the outline of the target object,the line elements was split quickly and accurately.Taking manual interpretation results as the standard,the effectiveness of the intelligent extraction method was verified by comparing the results of intelligent extraction with traditional extraction results in terms of accuracy evaluation,recognition effect,and parameter extraction.The results show that in terms of recognition accuracy,the ac curacy,recall rate,and cross to merge ratio of the intelligent extraction method reach up to 91.2%,85.2%,83.4%,and 72.9%,separately,while for those of traditional extraction method,the values are 89.9% and 62.1%,75.4%,and 51.6%,respectively.In terms of recognition effect,the intelligent extraction method has more intelligent ability in the detailed processing,and has more consistency and smoothness in the extracted lines,and also has less noise than traditional extraction method.In terms of statistical analysis in the length and orientation distribution,intelligent extraction and manual interpretation show a high degree of matching.Therefore,the intelligent extraction method based on MASK R CNN can automatically identify aromatic lattice fringes,which can reduce operating cost and improve working efficiency,and this can provide a reliable support for studying the coal macromolecular structure.