## 2 Methodologies  
### Overview  
Our model consists of five main modules in terms of structure, as shown in Figure 1, from bottom to top: the input embedding layer, the feature extraction layer, the feature fusion layer, the text representation layer, and the classification prediction layer. Figure 1: The overall structure of the proposed CAC model. The input embedding layer is responsible for converting the text into vector representations, capturing the semantic information of words. By mapping words to high-dimensional vector space, the word embedding layer represents words in the text as continuous and semantically similar vectors, helping the model understand the relationships and meanings between words. The feature extraction layer is the core part of the model, utilizing methods such as convolutional neural networks and attention mechanisms to extract local and global features from the text. The convolutional neural network extracts local features by sliding windows over the text, capturing the local relationships between words. The attention mechanism models the global interactions within the entire text sequence by learning the importance weights of different positions, enabling the model to focus on key words or phrases and capture the global semantic information. The feature fusion layer combines the local and global features obtained from the feature extraction layer to comprehensively consider information from different levels. By merging representations of different features, the model can better capture multi-level semantic information in the text and improve the accuracy and robustness of the classification task. The text representation layer encodes and integrates the fused features to generate a vector representing the entire text. By representing the text as a fixed-length vector, the model can transform texts of different lengths into a unified representation, facilitating subsequent classification prediction tasks. Finally, the classification prediction layer uses the SoftMax classifier to map the text representation to predefined categories, completing the text classification task. In addition, in the structural design of the model, we incorporate computational principles such as hierarchical structure, isolated computation, and parallel computation inspired by membrane computing. In terms of hierarchical structure design, we treat the input embedding layer, feature extraction layer, feature fusion layer, text representation layer, and classification prediction layer as independent hierarchical modules, each with its own computational units and structure. Each layer performs feature extraction in different feature spaces. In isolated computation, similar to the membrane-isolated nature of computational units in membrane computing, the local and global features of the text are computed separately. Different computational rules are applied in each feature extraction unit, and information is propagated, and feature fusion is achieved through interactions between membranes. In parallel computation, multiple parallel feature extraction modules are designed, with each module responsible for processing text information at different scales, thereby improving computational efficiency and model performance. To achieve richer feature representation, we employ different optimization strategies and transformation rules, enabling each layer to perform feature propagation in different feature spaces. Specifically, at the input layer, we enhance the sequential information of the input sequence using learnable positional vectors. At the feature extraction layer, we employ convolution and attention computations to capture both local and global relational features within the sequence. To reduce computational complexity, we simplify the stacked attention structure to support multi-layer computations. In the feature fusion layer, we merge the features obtained from both global and local outputs.