Aiming at the practical application requirements of high precision, lightweight and instant for Specific Emitter Identification(SEI), a lightweight model design for radiation source individual recognition of Automatic Dependent Surveillance-Broadcast(ADS-B) signal is proposed in this paper. Firstly, the signal data is decoded according to the characteristics of the signal data, and the weight of the unbalanced sample is adjusted to improve the sample quality. Then, the small features of different dimensions are obtained by grouping convolution and splicing with the initial features to realize multidimensional complementary feature fusion and parallel synchronization to improve the recognition efficiency. Network model compression and cross-layer connection are implemented by using a Ghost bottleneck structure, which tends to save computing resources while integrating multi-dimensional characteristics. The experimental results show that the proposed algorithm has the advantages of simple structure and low computational load, high recognition rate of 95.2%, and a stable recognition effect in different capacity samples. The proposed design better balances the needs of individual identification accuracy, lightweight and efficiency for SEI.