Classifying gravitational wave signals is an essential task in analyzing data from space collected by advanced tools such as an interferometer. In this paper, we present a new architecture of a convolutional neural network that classifies gravitational wave spectrograms into a selected class. For this purpose, a novel attention module based on the fuzzy controller architecture was proposed. The mechanism is based on the generation of matrices: query Q, keys K, and values V, where the first two are fuzzified by a Gaussian function and subjected to fuzzy inference. The inference results are sharpened and multiplied by values in V. This solution allows the use of the idea of a fuzzy controller to analyze features in neural networks. The model was tested and analyzed in terms of different evaluation metrics that show that this model can reach higher results than the state-of-the-art.