## 5 Conclusions  
This study focuses on the task of multi-label legal text recognition, innovatively constructing a multi-head hierarchical attention framework and supporting a new hierarchical learning optimization strategy. In terms of method construction, the framework achieves precise extraction of multi-level semantic representations of text and effective acquisition of multi-label category information through the collaborative operation of the feature extraction and hierarchical modules. Concurrently, the hierarchical learning optimization strategy successfully breaks the shackles of traditional methods in balancing multi-level semantic and multi-label category information learning, accelerates the convergence speed of framework training, and lays a solid foundation for efficient and accurate multi-label legal text recognition. In the experimental verification phase, the proposed method showed significant advantages compared with mainstream methods on the CAIL2021 legal field dataset and the general multi-label recognition datasets AAPD and WOS. The model can complete tasks more accurately and efficiently, whether it is multi-label recognition of complex legal text in legal scenarios or facing diverse text types in general scenarios. This highlights the strong generalization ability and adaptability of this method. However, the model can be further optimized in terms of cross-level label modeling, such as employing a learnable soft-linked hierarchical label association approach to enhance the robustness and generalizability of the model to real-world legal data. This approach improves the adaptability of the model to challenges such as category interference, label variations, and semantic ambiguity between different labels.  
The study findings are expected to be widely applied to the intelligent processing of legal information, such as assisting legal practitioners in quickly searching and classifying massive legal literature, improving the accuracy of intelligent classification of judicial cases, and further promoting the digitalization and intelligence process of the legal industry. Concurrently, this method provides new ideas for related NLP tasks. Future research can explore and expand its potential value to text processing in other professional fields.  
Conceptualization, K.Z.; methodology, K.Z., Y.T., J.L., Z.A. and Z.L.; software, K.Z. and L.W.; validation, L.W. and Z.A.; formal analysis, L.W. and K.Z.; investigation, K.Z., Z.L. and Z.A.; resources, L.W., J.L. and Z.A.; data curation, K.Z. and L.W.; writing--original draft preparation,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Recall Rate** & **Micro-F1** \\ \hline Ours -layer-BN & 70.23 & 53.63 & 60.82 \\ Ours -layer & 71.68 & 52.48 & 60.60 \\ Ours -BN & 68.70 & 54.83 & 60.99 \\ Ours & 67.57 & 56.06 & 61.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of the CAIL2021 dataset.  
K.Z., Y.T. and Z.L.; writing--review and editing, K.Z., Y.T., J.L., X.L. and Z.A.; visualization, J.L., L.W., X.L. and Z.A.; supervision, L.W.; funding acquisition, L.W. and K.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work in this paper was supported by the National Natural Science Foundation of China under Grant No. U23B2056, and the National Key Research and Development Program of China under Grant No. 2022 YFC3340900.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the datasets used in this research are publicly accessible.  
**Conflicts of Interest:** Author [PERSON] was employed by the company China Justice Big Data Institute Co., Ltd. Author [PERSON] was employed by the company China Satellite Network Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.