## 1 Introduction  
Deep learning is a branch of machine learning in artificial intelligence (AI) that uses artificial neural network architecture to learn from data and mimic human thinking patterns [1]. A recurrent neural network (RNN) is a typical deep learning model that takes in sequence data. It connects all units in a chain-like manner and processes them along a time-ordered or logic-ordered sequence [2]. The RNN has been broadly applied in natural language processing (NLP) fields, such as text classification, sentiment analysis, machine translation, and other time series analysis areas. For the text classification in NLP, one of the security threats in RNNs, i.e., adversarial text attacks, is increasingly attracting people's attention. Real-world application scenarios for adversarial text attacks include spam detection, harmful text detection, and malware checking [3; 4; 5]. More and more text classification systems are deploying RNNs with enormous layers and neurons, and security is significant for these systems. One of the troubles is that RNNs will produce results out of the attacker's unfriendly purpose with ulteriorly attacking, which may lead to a catastrophe [6]. The users or developers need to know what kind of abnormal behavior of RNNs is involved in the system, that is, providing clear explanations for the output results. With appropriate explanations, the classification results can be more acceptable, and the undesirable RNN model can be repaired under some guidance [6; 7]. Explainability is an important dimension for developing trustworthy RNNs, and it is also the guarantee for the large-scale application of RNNs. Like other deep learning models, it is almost impossible to fully understand the internal structural organizations of a large-scale RNN and clarify the communication pathsbetween its neurons [8]. When an RNN model contains vast layers, it is difficult to adopt a suitable mechanism to describe the decision-making process in the RNN. Even if we can list all the weights, inputs, and outputs of activation functions for each neuron node, we still do not know how those large and constantly changing numbers are organically combined to complete a prediction or decision-making task. Therefore, users either have little knowledge of what the system makes decisions based on or cannot afford the time and space costs to handle the dynamic changes and every static detailed parameter of RNN neurons. Providing explanations for RNN text classification results is an important and valuable research topic. In recent years, some works have attempted to achieve the explainability of RNN models through model abstraction techniques [9; 10]. Model abstraction can obtain a concise approximate model for an original RNN, which removes irrelevant or trivial information and keeps specific properties under consideration. Explaining RNNs on an abstraction model is beneficial. Firstly, the state space of the abstraction model is significantly reduced, which can effectively save time and space to visit the original RNN, especially when the dataset is large [11]. Secondly, some practical model analysis and verification techniques can be applied to the abstraction model to explain the original RNN [12]. This undoubtedly meets the inherent requirement of transparency in explainability principles. Thirdly, it is difficult and costly to reverse engineer an abstraction model to reproduce an RNN model, which provides reliable protection for the original RNN [13]. The main challenges in applying model abstraction to explain RNNs are as follows. Firstly, it is not easy to map the specific insertion patterns with neural activation, unlike convolutional neural networks (CNNs), which have a fixed number of neurons and play a particular role in feature extraction. The network layers in RNNs typically cannot maintain the same potential spatial functions due to their dynamic behavior over time, and the length of different input sequences varies with the dynamic behavior over time.