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To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch. . To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch. . To address this problem, this paper proposes a GRU recurrent neural network based mobile communication base station traffic prediction model and improvements. Using the open-source sample dataset provided by the organising committee of the China Universities Big Data Challenge 2021, the study was. . Predicting base station network traffic has high practical guiding significance for network research, management and control. Aiming at the problem of accurate prediction of base station traffic, this paper proposes a gated recurrent unit neural network model (GRU model) based on neural network. . Using AI models to accurately predict base station traffic not only helps information and communication infrastructure save energy and reduce carbon emissions, but also reduces operators investment costs and promotes the green and low-carbon development of mobile communications. In order to reduce. . In order to reduce and reduce the error of predicting network flow data, a neural network algorithm prediction model based on machine deep learning, long and short memory network flow prediction model, which can predict the base station flow data according to the periodicity and volatility. . In order to better cope with the overall network efficiency and energy consumption caused by the tide phenomenon of the network traffic of the base station and the physical capacity expansion caused by the increasing network traffic demand, we need to predict the network traffic of the base station. . Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies.
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