Furthermore, a hierarchical multiscale feature extraction and fusion strategy is implemented to progressively integrate coarse-grained global context with fine-grained local patterns, thereby improving the discriminative capability for subtle inter-class variations in highly dynamic scenarios. In our work, we introduce a novel self-attention mechanism to address the challenges of effectively modeling multi-view sequences for 3D object recognition. As shown in Figure 1, the self-attention mechanism operates bidirectionally across two key dimensions: spatially, it processes feature maps within the same frequency space of individual views to comprehensively model spatial relationships, while temporally, it analyzes sequential views in the shared feature space to capture dynamic inter-view patterns. By integrating Spatial and Temporal Self-Attention, structural attention ensures that features extracted from the same appearance feature space are coherently modeled across both dimensions, enhancingthe network's ability to learn discriminative representations for 3D object recognition. This dual-dimensional attention mechanism not only improves feature expressiveness but also establishes a computationally efficient framework for processing sequential views in dynamic scenarios. We evaluated our MSSAViT method on the ModelNet datasets for 3D shape classification tasks. In addition, the experimental results demonstrate that MSSAViT achieves promising improvement on 3D shape classification and comparable performance with recent methods using varying types of 3D data. The key contributions of our work are as follows:  
1. We propose a lightweight multi-view classification model. We present CNN-ViT, a lightweight hybrid architecture integrating Structural Self  
Figure 1: Spatial and Temporal Sparse Structural Self-Attention. After performing the ordered observation and rendering of 3D object models to obtain continuous view images and using a 2 DCNN backbone network to extract features from each view image, followed by the feature channel alignment of the continuous view feature maps to ensure that the temporal features in each channel have the same frequency characteristics, the spatial structured feature learning is carried out for the feature maps of different channels in the same frame, and temporal structured feature learning is performed for the feature maps of the same channel in the temporal frames, thereby achieving the joint spatial-temporal modeling of the sequential view images. Attention mechanisms to jointly model spatial and temporal structural patterns in sequential view convolutional features. The modular design enables efficient feature extraction and fusion. 2. We improve Sparse Structured Self-Attention for computational efficiency. To address the computational limitations of self-attention mechanisms, we introduce an improved Sparse Structured Self-Attention module. By employing a learnable sparse mask to restrict Query-Key connections, the proposed module dramatically reduces computational complexity while maintaining high expressiveness. This design not only alleviates the computational burden but also prevents overfitting caused by redundant feature modeling, ensuring a balance between efficiency and accuracy. 3. We realize Multiscale Structural Feature Representation. In our work, the SSSA and TSSA modules progressively reduce the resolution of feature maps. This architectural design significantly reduces computational costs and parameter counts while aggregating multiscale feature representations. The resolution-reducing Structural Self-Attention mechanism enhances both the efficiency and representational capacity of the model, providing a compact yet expressive feature representation for multi-view classification tasks. This paper is organized as follows. Section 2 systematically reviews related works in 3D shape classification recognition. Section 3 provides the detailed configuration of our proposed methodology.