(Aim) Alcohol use disorder may put health at risk and cause serious health problems. It is of increasing importance to identify alcohol use disorder as early as possible. (Method) This study proposed a computer-vision based technique. The dataset was scanned by magnetic resonance imaging in China participating hospitals. Afterwards, we combined wavelet entropy, two-layer feedforward neural network, and cat swarm optimization (CSO). The CSO mimics the behavior of cat and is composed of two modes: seeking mode and tracing mode. (Results) The results showed that our method achieves a sensitivity of 91.84 ± 1.66%, a specificity of 92.40 ± 1.22%, and an accuracy of 92.13 ± 0.70%. Using grid searching approach, we found the classification performance is the best, when decomposition level is assigned with 2 and the mixture ratio is assigned with a value of 0.8. (Conclusion) The CSO is superior to four bioinspired algorithms: genetic algorithm, immune genetic algorithm, particle swarm optimization, and chaotic self-adaptive particle swarm optimization. In addition, our proposed alcoholism identification system is superior to four state-of-the-art alcoholism detection approaches.