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It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive. However, these behaviors can both lead to some similar market anomalies, such as excessive trading volume and volatility in the stock market. Due to the limitation of traditional time series analysis, we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method. Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance, we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network. Then, we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor, respectively. Using stock transaction data of Microsoft, US S&P 500 stock index, and China Hushen 300 stock index, we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network. Finally, we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.