###### Abstract
The research is significant in that it could result in the development of more accurate, robust, and interpretable text classification systems, which are essential for a variety of applications in NLP. This hybrid approach demonstrates the potential for combining different models to leverage their unique strengths, thereby improving performance in complex text analysis tasks [14; 15; 16]. The objective is to enhance the efficacy of event text classification and event assignment through the utilization of Graph Convolutional Networks (GCN) and multi-attention mechanisms. GCNs are designed to operate on data represented as graphs, where nodes represent entities (such as words, phrases, or events) and edges represent relationships between these entities. In the context of event text classification, GCNs can be employed to model the relationships between disparate entities mentioned in the text, such as the connections between different events or the relationships between events and specific entities (e.g., people, and locations). GCNs facilitate the capture of structural dependencies and relational information which are essential for comprehending the context and categorization of events in a more comprehensive manner [17; 18; 19]. The aforementioned research indicates that there are many methods for enhancing the precision of text classification models by integrating the Bert model with various deep learning models. However, in this work, we integrate the [PERSON] model with the RNN model, which represents our primary proposed model in this paper. The combination of the two models results in an enhancement in text classification due to the complementary strengths of both models. [PERSON] offers a profound, bidirectional comprehension of words, elucidating their meanings based on the context of surrounding text. [PERSON], on the other hand, demonstrates exceptional proficiency in processing sequential information and discerning the order and flow of words within a sentence. The integration of [PERSON] with the RNN model provides the following benefits:  
* Bidirectional Context: [PERSON] offers a comprehensive, context-based understanding of word meanings. * Sequential Processing: [PERSON] is particularly adept at identifying the order and flow of words in a sequence. * Enhanced Accuracy: Combining [PERSON]'s contextual embeddings with RNN's sequential modeling improves text classification accuracy. * Improved Generalization: The combined model enhances the ability to generalize across different text types. The remainder of this paper is structured as follows: Section 2 presents the related work concerning various text classification models. Section 3 presents the proposed methodology, which will cover Bert and the proposed RNN_Bert_based model structure. The proposed solution flowchart and algorithm are also included in Section 3. Section 4 presents the simulation settings, parameters, and the existing work on text classification. It also explains the experimentation of the proposed RNN_Bert_based algorithm, simulation setting, and its result. Finally, a conclusion of our work contributions and the significant task for future research are presented in Section 5.