## 3 Method  
### Overview  
To enhance the informatization and organizational efficiency of document management, this study proposes AttenFlow, a novel context-aware architecture designed to automate document circulation and processing [33, 34]. The AttenFlow architecture, illustrated in Figure 1, leverages advanced information technology to standardize key document management processes, including registration, drafting, review, and publication, thereby enabling automated document processing and transmission. AttenFlow adopts a multi-layer design to ensure system efficiency and scalability, consisting of a hardware and network communication layer, a basic service layer, a business logic layer, and a user interface layer. The network communication layer enables flexible system access via multiple networking methods, while the hardware platform provides essential computing and storage support. The basic service layer manages system access, data processing, and database storage using standardized data processing models to store and manage document data following established protocols. In the business logic layer, the system defines document classification criteria based on factors such as time, confidentiality level, and content, while customizing workflows and approval processes according to user requirements. Figure 1: AttenFlow structure diagram of context-aware architecture for automating official document process processing. The primary goal of AttenFlow is to enable seamless document circulation and efficient transmission by streamlining business processes, minimizing manual intervention, and improving overall document management efficiency. By integrating advanced networking technologies, AttenFlow facilitates flexible data transmission across geographically distributed users while ensuring the security and integrity of documents during circulation. To achieve these objectives, this study focuses on two core technical innovations that address the fundamental challenges in document flow prediction and classification. The first innovation is the retriever consensus confidence fusion (RCCF) method, which enhances document retrieval accuracy by dynamically adjusting fusion strategies based on retriever consensus. Traditional hybrid retrieval fusion approaches often suffer from fixed weighting schemes and lack confidence estimation capabilities. RCCF addresses these limitations by incorporating consensus measurements between different retrievers, enabling adaptive weight assignment and providing confidence scores for retrieval results. This mechanism effectively improves retrieval accuracy while reducing computational overhead for large-scale document datasets. The second core innovation is the AM-HDGAT for text (adversarial mutual-attention hybrid-dimensional graph attention network for text) module, which revolutionizes document classification by modeling inter-document relationships through graph structures. Unlike traditional approaches that treat documents independently, AM-HDGAT constructs document graphs to capture semantic relationships and leverages both high-dimensional semantic features and low-dimensional statistical features through a mutual-attention mechanism. Furthermore, adversarial training is incorporated to enhance model robustness against potential attacks, making it particularly suitable for security-critical document processing applications. The synergistic operation of these components is achieved through three fundamental mechanisms:  
1. Retrieval-Classification Pipeline: The RCCF module processes retrieval results to generate quality-assured document subsets with confidence scores. These outputs serve as structured inputs for the AM-HDGAT classifier, where RCCF confidence values initialize node weights in document graphs constructed using k-NN similarity metrics (\(A_{ij}=1-\text{Cosine}(RCCF\_Score(d_{i},d_{j}))\)). 2.