## 1 Introduction  
Machine intelligence is increasingly important in global poverty governance [1, 2]. By using algorithms and models, social scientists and policymakers can embrace more substantial insights into the essence of poverty, using complex phenomena and vast amounts of data to prevent vulnerable populations from falling into the cycle of poverty [3]. From the viewpoint of the digital governance of poverty [4], unstructured text data have become a significant part of poverty governance data. These documents contain policies, news announcements, working papers, etc. [5]. At the same time, each class includes a tree of subclasses, adding much information related to certain aspects of the poverty governance lifecycle. Usually, researchers give these texts multiple labels in line with the above hierarchical classes via time-consuming manual efforts before analyzing this information and extracting knowledge about poverty governance [6, 7]. Therefore, a classification model based on natural language processing is the primary method for automatic multi-label classification [8]. According to the existing literature, a multi-label classification model can be divided into two parts based on the number of classifiers in the model. First, a single-classifier model transforms the multi-label classification into a multi-class classification problem [9], which is concise and considers labels' correlation as a whole, but usually has an unsatisfactory effect because of the imbalanced training dataset [10]. As the number of labels increases, the parameter size and complexity of the model also significantly increase. In contrast,combining multiple classifiers [11], namely one or a subset of labels corresponding to a classifier, could reduce the complexity of a single model and avoid being influenced by the dataset's quality, yielding a better holistic classification result. Nevertheless, the number of classifiers will increase while the labels are abundant. In the classifier set, the correlation of each classifier can be independent or dependent on the relationship between related labels. The former transforms the multi-label problem into several single-label issues, ignoring label correlation. The latter regards a combination of classifiers as a chain of classifiers. The advantage of classifier chains is that they construct topological relationships between independent classifiers based on the semantic topological relationships of labels and form a chain structure. However, excessively long classifier chains can lead to a systematic accumulation of classification errors, affecting the end labels' classification accuracy [12]. It is necessary to ensure that each classifier in the classifier chain can achieve the best accuracy to remedy this problem. From a process perspective, each text classifier includes four modules: data preprocessing, feature extraction, classification algorithms, and result evaluation [13]. To improve the performance of the classifier chain, researchers typically perform unified preprocessing on all classifier inputs and a unified assessment of the classification results [11]. Feature extraction and classification are two kernel processes that determine the performance of a classifier. The methods for obtaining text features are usually divided into metadata-based and content-based methods [7]. The metadata-based approaches utilize explicit information such as titles, authors, keywords, categories, and other existing labels in the text as text features. This method is standard for the scientific literature with complete metadata information [11], but for text in poverty governance, metadata information is often not a mandatory configuration. Therefore, content-based feature extraction methods are more valuable in PGT text classification. In the existing literature, content-based text feature extraction mainly includes traditional statistical methods such as word bags and TF-IDF, neural network methods such as Word2 Vec, and pretrained models such as BERT, ERNIE, and XLNet [14].