## 1 Introduction  
The widespread adoption of mobile networks has led to a dramatic rise in the number of Internet users in a short period of time. Additionally, the rapid development of the Industrial Internet has driven a sharp increase in the number of mobile devices accessing the Internet and the demand for related applications [1]. Due to the gradual depletion of radio spectrum resources, Wireless Fidelity (Wi-Fi), which operates at 2.4 GHz, has a limited spectrum bandwidth, which restricts its ability to accommodate the rapidly increasing data traffic and bandwidth demands. Visible light communication (VLC) offers abundant spectrum resources and combines lighting and communication, and it has the potential to become the next generation of wireless communication technology [2]. The heterogeneous network formed by VLC complementing Wi-Fi can provide indoor users with faster, greener, and more reliable communication services. However, since the coverage areas of the two networks overlap with each other in real scenarios, the random movement of users between different access points will lead to frequent network handovers. Research on efficient user movement prediction algorithms and solving the problems of the reasonable selection of network access points have become important challenges for heterogeneous communication networks. Depending on whether the network belongs to the same technology mode before and after the handover, the handover mode can be categorized into two types [3]: vertical handover (VHO) and horizontal handover (HHO). HHO refers to the handover between the same technology, while VHO refers to the handover between different technologies. In indoor VLC/Wi-Fi heterogeneous networks, there is more literature on optimizing the VHO problem, and all of these studies have achieved specific results. [PERSON] et al. proposed a handover decision mechanism based on channel-adaptive dwell time, which dynamically adjusts the dwell time to adapt to changes in the network environment [4]. [PERSON] et al. proposed a hybrid application-aware VHO scheme that combines network state and application requirements (e.g., delay, bandwidth) to optimize the handover decision [5]. [PERSON] et al. predicted VHO using a hidden Markov model [6]. [PERSON] et al. proposed combining a hierarchical analysis method with a cooperative game model to decide whether to perform VHO [7]. The VHO decision scheme implies a direct transfer to Wi-Fi for communication when the quality of VLC communication is degraded or interrupted. However, too many connections can easily lead to Wi-Fi overload and reduce system performance. To solve the network selection problem, [PERSON] et al. proposed a centralized handover scheme using the statistical information of channel blocking [8], which employs optimization techniques to maximize the overall system throughput. Some scholars proposed using fuzzy logic [9] and artificial neural networks [10] in indoor heterogeneous networks. These methods optimize handover decisions using system parameters such as channel state information, user speed, and data rate. However, fuzzy logic rules must be predefined and adjusted, increasing practical applications' complexity and maintenance cost. Artificial neural networks need many iterations to reach a stable state. An analysis of the above literature reveals that none of these studies consider the actual scenarios of mobile users. Additionally, they all suffer from high computational complexity and frequent handovers. To better adapt to the dynamic needs of mobile users, trajectory prediction techniques can be introduced into indoor heterogeneous networks, avoiding the problem of frequent handovers caused by relying on a single piece of information (e.g., received signal strength) in the traditional methods. In indoor space, the density-based spatial clustering of applications with noise (DBSCAN) algorithm [11; 12] is mainly used to mine mobile users' trajectory features. [PERSON] et al.