Detecting the composition of network nodes and analyzing their characteristics is essential for improving the stability and security of the Bitcoin network. Existing research mainly focuses on the analysis of network node attributes, with less attention given to the optimization of the node detection method itself. Existing Bitcoin network node detection methods have limitations, such as long detection time and high overhead. In this study, the existing methods are generalized into a full traversal without deduplication (FTWD) measurement model, which is evaluated through a large number of experimental measurements. The main factors affecting detection time, overhead, and accuracy are analyzed. Based on this, an improved Bitcoin network node probing method, Bitcoin node probe (BNP), is proposed. This method reduces probing time and overhead and improves probing efficiency by increasing the number of seed nodes in the initial rounds, introducing an indicator for the proportion of new nodes added between rounds, and adopting a partial traversal strategy. Experimental results show that, compared to existing methods, the BNP method reduces probing time by 40.4% on average and probing packet overhead by 21.4% on average when the random selection ratio is 50%, although the total number of probing nodes decreases slightly.