For the increasingly serious phenomenon of electricity theft, many researchers are trying to detect it. Traditional detection methods rely on physical inspection, which has low detection efficiency and high cost. However, the rapid application of advanced metering infrastructure (AMI) makes it possible to detect electricity theft via smart meters. In this work, we propose a hybrid improved wide and deep convolutional neural networks (CNN) method to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China. Our key contribution is the introduction of a channel-dimensional adaptive attention module concatenated with dilated convolutions. Moreover, we propose focal loss to solve the data imbalance problem. Experimental results demonstrate that compared to other state-of-the-art deep learning classifiers, our improved method has better performance (with 97.08% of MAP@100 and 83.61% of AUC), which verifies the effectiveness of the proposed method.