The rapid identification of radionuclides is a critical component of nuclear material detection systems，essential for improving the performance and efficiency of radiation detection. Traditional nuclide spectrum recognition methods typically involve multiple complex steps，such as noise reduction，background subtraction，and feature extraction，which are computationally intensive，time-consuming，and inefficient，making them unsuitable for rapid response in practical applications. To address these issues，this paper proposed a rapid nuclide spectrum recognition algorithm based on the MobileNetV3 neural network，which achieved efficient nuclide recognition by optimizing data processing and model training methods. A series of simulated datasets were generated using Monte Carlo (MCNP) simulation software，including scenarios with different radioactive sources and particle counts，varying distances between NaI detectors and the sources，and mixed nuclide environments. These diverse datasets were used to train and validate the network model，enhancing its generalization capability. To better process the full-energy peak characteristics of gamma spectra，this study designs a preprocessing method based on a sliding window approach，which incrementally transforms one-dimensional spectral data. Subsequently，the transformed spectral data is mapped into two-dimensional grayscale images using Hilbert curves and input into the MobileNetV3 model for training and prediction. Experimental results demonstrate that the proposed neural network model performs exceptionally well in rapidly processing spectrum data handled by the sliding window method，achieving high-precision recognition of different nuclides while maintaining efficient learning. In terms of model performance，using sliding window sizes of 23 and 25 results in faster convergence and significantly improved recognition accuracy. This study highlights the effectiveness of integrating deep learning with nuclide spectral characteristics，providing a novel and efficient solution for nuclear material detection systems.