Taiwan is an island state characterized by well-developed maritime traffic and sea transportation. Smuggling by sea increasingly threatens the country's social and internal security. The Coast Guard Administration is the main actor intercepting smuggling. This study applies classification trees and Bayesian network algorithms to create dependent and independent classification tree models and Bayesian network models for improving the smuggling vessel recognition rates. We focus on relationships of dependence among smuggled goods (for example, fishery goods, firearms and drugs) and variables (such as information on the home port, the age of boat owners, as well as the arrival and departure dates of vessels). The paper presents a selection method for vessels which could be applicable in a number of other maritime instances, such as, for instance, Port State Control inspection. Our findings help in improving recognition accuracy and reduce the potential harm to social and national security by facilitating the identification of vessels with the largest smuggling value.