## 1 Introduction
[9] studied the maximum connected component in MANETs, using the proportion of the maximum connected component as an important indicator to measure network connectivity; Ref. [10] analyzed the network connectivity of MANETs under the RWP mobility model and obtained the optimal communication radius of nodes when the network satisfies k-connectivity, which is of great significance for improving the robustness and survivability of the network. Reference [11] investigated three connectivity indicators of MANETs, including node degree, average node degree, and maximum node degree, under shadowing effects, and the results provided important insights into improving connectivity and optimizing routing protocols in shadowing environments. Most of the existing research focuses on establishing different node distribution computing models to analyze the characteristics of the network. Currently, most studies on the design of indicators for describing node mobility in mobile models are relatively simple and mostly consist of the same type of indicators. The computational and analytical complexity of the mathematical models is relatively high and is only applicable to a single mobility model in most cases. Owing to the lack of generality, it is difficult to accurately quantitatively analyze or compare the characteristics of various mobility models. On the other hand, the analysis of the impact of mobility on network performance and the relationship between different mobile models is mostly qualitative. For example, in [5], each mobile scenario corresponds to one curve in network performance. If the movement scenario is more complex, a large number of simulation experiments are required to obtain the relevant results. Deep learning (DL) is a hot research topic in the field of modern computer science [12]. It has been decades of development, from theoretical research to industrial applications. DL has obvious advantages in solving problems with complex model building. Comparedto traditional machine learning (ML) algorithms [13], it can automatically learn the deep-seated characteristics of the data. Nowadays, it has been widely used in biomedical, natural language processing, image recognition, and other areas. In the field of communication, introducing DL technology into diverse aspects of wireless communication has been proven to significantly improve the performance of wireless communication systems [14; 15; 16; 17; 18]. Compared with other intelligent optimization algorithms, neural networks have the benefits of nonlinearity, input-output mapping, adaptability, fault tolerance, high-speed parallelism, and self-learning, which will definitely provide critical technical support for the development of wireless communication. The UAV mobile wireless network is a large and complex system; the indicators that can be utilized to describe node motion relationships are complex and diverse, which also have correlations, diversity, and ambiguity characteristics between them [19]. Quantitative or qualitative analysis of network performance is complicated and generally less applicable by using these methods. Therefore, our research proposes a backpropagation-neural-network-based [20] network performance classification method (BPNN-PC). Based on the construction of motion indicators for mobility models, our approach uses five designed motion parameters as inputs to the BP neural network, and the performance of the network under the same motion parameters is introduced as labels to conduct training of the BP neural network, Finally, the BP neural network is used to map the relationship between the mobility index values and the network performance. It can solve the problems of traditional mathematical derivation-based methods, which rely on mobility model types, overly intricate descriptions of relationships between indicators, and low generalization.