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Coastal engineering construction suitability evaluation methods are too empirical and difficult to quantify. Considering these weaknesses, in order to determine the weight of each factor reasonably, and to analyze the suitability of coastal zone construction comprehensively, the theory of establishing a coastal zone construction suitability evaluation model based on a Rough Set (RS) and an Analytic Hierarchy Process (AHP) is proposed. In total, 20 typical coastal areas of Putian are selected, and the main impact factors are determined according to a port dock, pollution-prone industry, and an electric power plant. The contribution rate and weight of each factor for the construction of a coastal zone are analyzed by the combination evaluation model, and the final evaluation result is consistent with the actual investigation situation. Finally, 52 evaluation units of the Putian coastal zone are evaluated by Neural Networks (NNs). The weight of the impact factors is made more objective by using the training sample set of the combination evaluation model as the sample set of the neural network. The learning speed and accuracy of the network are improved, and the and the evaluation result is consistent with the actual investigation situation. In a word, it is effective to perform the suitability evaluation of the coastal zone construction using the RS-AHP-NN proposed model, and it can be applied in practical engineering.