In order to analyze the interactive relationship between the influencing factors of ship collision accidents more scientifically and reveal the evolution mechanism of ship collision accidents, a ship collision accident database was established based on the global ship collision accident report, which included five types of influencing factors: human factors, ship factors, management factors, environmental factors, and accident time. The Apriori association rule mining algorithm was used to determine frequent patterns, associations, co-occurrences, and causal relationships among these influential factors. Visual representations of these results were obtained by using complex network theory. The topological analysis methods,  important node sorting algorithm based on mutual information theory and edge sorting algorithm based on the centrality of edge mediations were used to identify critical influential factors and edges within the network, and evaluate their robustness. The results indicate that the influencing factors of most ship collision accidents are relatively active and the interaction network of influencing factors is closely connected, and factors such as ship tonnage, age, and navigation water area are more important in interactive information transmission.