To tackle the challenge of multiple concurrent fault localization in analog circuits, we propose a localization algorithm that integrates both real and complex domains. In the real domain, comprehensive data features are extracted by employing various operations of spatial and channel attention mechanisms, while simultaneously managing network depth and parameter amount. Within the complex domain, we capitalize on the disparities emerging across layers within a complex-valued convolutional neural network. Through skip connections, a deep-shallow feature fusion structure is formulated, ensuring that shallow information susceptible to loss is preserved and integrated with deep information to yield complex-domain features. The integration of real and complex domain features is subsequently applied to the research on composite fault localization in analog circuits, yielding an average localization accuracy exceeding 93% and a peak accuracy reaching 100%. This approach demonstrates robust stability and reliability, furnishing a viable solution for the ongoing research on composite fault localization in analog circuits.