<p>Language identification is a core technology in the field of multilingual information processing, and its goal is to automatically identify the corresponding language by analyzing speech signals. However, existing approaches face three major challenges: (1) the limited representation capacity of single-scale features for complex speech patterns, (2) suboptimal model performance caused by single-objective optimization, and (3) the computational intensity of processing high-dimensional speech data. To effectively address these computational challenges, powerful supercomputing capabilities have become the key support for advancing research. Based on this, this study innovatively proposes a method based on multi-scale features recursive fusion and adaptive loss, which is referred to as MSFRF-AL. Specifically, the wav2vec2.0 pre-trained model is first used for feature extraction. The extracted features are then sent to the multi-scale feature recursive fusion network (MSFRF-Net) for fusion from different time scales. Then, the fused features are sent to the simple back-end architecture composed of the statistics pooling layer and the fully connected layer. Finally, the proposed adaptive loss is employed to optimize the model training process. This objective function consists of cross-entropy and efficient triplet loss, combining classification ability and feature representation ability to simultaneously enhance feature discrimination and classification accuracy. Experiments are verified on three tasks of the OLR2020 dataset. The experimental results show that this method can effectively reduce the interference of the external environment on language identification, especially demonstrating stronger robustness in cross-channel and noisy environments, and significantly improving the performance of language identification.</p>