In intelligent transportation systems, distributed acoustic sensing offers unparalleled advantages in monitoring and analyzing vehicle characteristics and behaviors in real time over the entire optical fiber. In this work, an accurate and efficient φ-optical time domain reflectometer-based load recognition method for light and heavy loaded vehicles is proposed. Before load recognition, wavelet denoising and 1D-mean filtering methods are used to denoise the signals; then the Mel spectrograms of the signals are extracted as the features input to the load recognition model with a backbone of EfficientNet convolutional neural network. The validation results show that, using an ∼47 km sensing optical fiber, the recognition of light and heavy loaded vehicles can well meet the needs of real-time data analysis and decision making of intelligent transportation, with an average recognition accuracy of 97.81% within 14 ms for each recognition.