False data injection attacks disrupt the stability of power systems by tampering with the data collected by data acquisition and monitoring control systems. Traditional methods for detecting false data injection attacks are unable to locate the attacked location or have low accuracy. Firstly, an improved method for detecting false data injection attacks using seagull optimized convolutional neural networks is proposed. The proposed method uses a convolutional neural network with shared weights and local connectivity to efficiently extract and classify features from high-dimensional historical measurement data. Secondly, an improved seagull optimization algorithm with balanced global and local search capabilities is introduced to perform hyperparametric optimization to obtain a highly matched network structure for false data detection. The network structure is then used to detect and locate bad data. Finally, the effectiveness of the proposed method is verified through extensive attack detection experiments on IEEE-14 and IEEE-57 node systems, and compared with various other detection methods to verify that the proposed method has better classification performance, higher accuracy, precision, recall, and F1 value.