Abstract: The index enhancement strategy as an organic combination of active investment and passive investment has attracted more and more attention from investors. Current index investment mainly uses methods such as machine learning to mine factors, ignoring first-hand information such as financial annual reports. This paper proposes an index enhancement strategy based on the fundamental data of accounting statements and central research of network science. First, the random forest method is used to select representative indicators in the company's accounting statements in the industry index. Second, the inter-company network is constructed based on the Pearson similarity of the indicators. Finally, the network centrality index is used to select highly central companies for portfolio investment. Research on a total of 456 stocks in 5 industry indexes shows that the return rate of the investment portfolio constructed in this article is higher and more stable than the benchmark return rate of the index. Among them, the semiconductor index's combined return rate selected in the 2019 semi-annual report is 100.37% higher than the benchmark return rate. This shows that the method has certain reference value and applicability for the research of index enhancement strategy.