Overharvesting of terrestrial and marine resources may be alleviated by encouraging an alternative configuration of livelihoods, particularly in rural communities in developing countries. Typical occupations in such areas include fishing and farming, and rural households often switch livelihood activities to suit climate and economic conditions. We used a machine-learning tool, deep-belief networks (DBN), and data from surveys of a rural Philippine coastal community to examine household desire to change livelihood. This desire is affected by a variety of factors, such as income, family needs, and feelings of work satisfaction, that are interrelated in complex ways. In farming households, livelihood changes often occur to diversify resources, increase income, and lessen economic risk. The DBN, given its multilayer perceptron structure, has the capacity to model nonlinear relationships among factors while providing an acceptable degree of accuracy. Relative to a set of 34 features (e.g., education, boat ownership, work satisfaction), we examined the binary response variables desire to change work or not to change work. The best network had a test-set accuracy of 97.5 percent. Among the features, seven significantly affected the desire to shift work: ethnicity, work satisfaction, number of persons in a household in ill health, number of fighting cocks owned, fishing engagement, buy-and-sell revenue, and educational level. A cross-correlation matrix of these seven features indicated households less inclined to change work were those engaged in fishing and retail buying and selling. For fishing, providing economic and other incentives should be considered to encourage changing from this occupation to allow recovery of fishery resources. Article impact statement: Deep-belief networks identify psychological, cultural, and economic determinants of livelihood switching to manage resource exploitation.