After decades of vigorous development, data mining technology has achieved fruitful theoretical and application results. As a highly applicable subject, data mining technology has penetrated into various fields of the national economy, and has aroused great attention from academia and industry. A large amount of chart data is stored in the electronic chart database, and its application is very extensive, providing a valuable decision basis for managers in all walks of life. It is of great significance to establish a complete data management mechanism based on data mining technology. The traditional data analogy extraction technology, because of the data association index and the poor ability of data association, leads to the difference between the extraction data and the target data. Therefore, the application of data mining technology on electronic chart data management is studied. Data mining technology uses rough set to obtain the basic information of electronic chart data management according to similarity function, mining electronic chart data management association rules; through the comprehensive evaluanon data system of electronic chart data management, building rule base, setting up the evaluation index of electronic chart data management, achieving the similarity evaluation of the mining results. Experimental test results: compared with the traditional data analogy extraction technology, the results obtained by data mining technology have higher similarity with the target data and meet the requirements of electronic chart data management acquisition. It can be seen that this technology is more suitable for the application of electronic chart data management

Abstract
1. Introduction
2. Standard Data Structure Analysis of Electronic Chart
    2.1.International Standards for the Application of the Electronic Chart
    2.2 Comparison of Data Structure of Electronic Chart
    2.3.Research on Data Structure of Electronic Chart
3. Methods and Techniques of Data Mining in Electronic Chart
    3.1 Discrete Data Mining Algorithm for Electronic Chart
    3.2 Algorithm Thought
    3.3Algorithmic Description
    3.4Experimental Results of Algorithm
4. Conclusions
References

• Haoran Song(MaritimeCollege ,Shandong jiaotong university)