The power sector can collect user electricity information quickly and accurately using computer vision technology. Given the problems of low precision and slow speed of traditional algorithms, a recognition algorithm of electricity consumption information based on an adaptive Bezier curve network is proposed. The framework integrates detection and recognition, and realizes end-to-end text location and prediction. The detection end extracts the feature from the input image by combining a feature pyramid and residual networks, and generates a Bezier curve through four control points, which can better fit the text box. A convolutional recurrent neural network is adopted at the recognition end, and the gate recurrent unit is introduced to replace the long short-term memory network, and is then combined with the attention mechanism to recognize the text. Finally, five ablation experiments are carried out on the data set for performance comparison and evaluation analysis. The results show that the recognition accuracy of the algorithm is up to 99.08%, and the reasoning speed is fast. It can be used in the practical application of electricity consumption information detection and recognition. This work is supported by the National Natural Science Foundation of China (No. 61976042).