False data injection attack seriously endanger the safety and stability of the power grid operations. Due to the high dimension and complex characteristics of the electricity measurement data, the attack locational detection accuracies of the existing methods are insufficient. For this reason, a false data injection attack locational detection method based on relevant features multi-label cascade boosting forest is proposed to locate the attacked position of the power grid. The proposed method enhances the fitting ability of the multi-label cascade forest processing the complex electricity measurement data by incorporating the extreme gradient boosting algorithm, so as to identify the abnormal state variables of each bus. Furthermore, the "relevant features" algorithm is integrated to extract the highly informative features from the original electricity measurement data to improve the generalization ability of the multi-label cascade forest, so as to obtain more accurate location detection. The simulation results on IEEE 14-bus and IEEE 57-bus test systems verify the effectiveness of the proposed method, and compared with many other methods, the proposed method has better accuracy, precision, sensitivity and F1-score.