## 1 Introduction
Since Google proposed BERT in 2018, with the continuous introduction of new models and methods, the performance of pretrained models has significantly improved owing to their unsupervised training on massive corpora [15]. As a result, pre-trained models have gradually become mainstream compared with other feature extraction methods. The basic structure of the BERT model consists of multiple layers of Transformers, including the \"masked language model\" (MLM) and \"next sentence prediction\" (NSP) as pretrained tasks. The most prominent feature of the ERNIE model is the introduction of a knowledge map as information outside the text to supplement the semantics of words. The state-of-the-art version of ERNIE has reached 3.0 [16]. Unlike the BERT and ERNIE models, based on the BERT model, the XLNet model replaces the traditional Transformer with Transformer-XL [17] and introduces two improved methods, namely the \"permutation language model\" and the \"two-stream self-attention\" mechanism, to achieve better performance [18]. Text is a type of temporal dataset. Before the emergence of attention mechanisms, models such as LSTM and RNN were widely used for the semantic extraction of temporal data [19]. However, these models always suffer from the problem of gradient disappearance as the text becomes too long. Attention mechanisms can avoid the problem of gradient disappearance and better understand text semantics [20]. However, the labels of poverty governance texts (PGTs) have a hierarchically fixed relationship. Moreover, for a specific training dataset, the label that reaches the relation tree leaves has less simple data [10]. Therefore, an unbalanced training dataset results in poor holistic classification accuracy. Because the current research results cannot solve the multi-label classification problem in the above situations better, this study focuses on hierarchical labels and limited training datasets to conduct multi-label classification research on PGTs. The research innovation of this article mainly lies in the following four aspects: (1) designing a hierarchical label tree for PGTs and constructing a standardized HTMC-PGT corpus through preprocessing and data labeling; (2) proposing an overall model to solve the hierarchical and transferable multi-label classification of PGTs (HTMC-PGT); (3) verifyingthe optimality of feature extraction based on XLNet for poverty alleviation governance text classification; and (4) proposing a transfer learning mechanism between hierarchical label classifiers based on an attention mechanism integrated with Bi-LSTM. Based on the results of this study, HTMC-PGT can achieve better hierarchical multi-label classification effects with limited training datasets and provide data support for further research on the policy analysis and knowledge graph of poverty governance [5; 21; 22]. The rest of the paper is organized as follows: Section 2 highlights the hierarchical label tree of PGTs and the formal definition of the HTMC-PGT problem; Section 3 presents the primary models and methods utilized in this study; Section 4 introduces and discusses the process, methods, and results of the experiments; the final section concludes the paper.