In this paper, the technical aspects of big data processing of multimedia communication are deeply studied. Firstly, we propose a cloud implementation method of radial basis function neural network which is based on Map-Reduce on cloud computing cluster. Secondly, in order to meet the needs of big data processing of multimedia communication, the map-Reduce-based error back-propagation algorithm is trained to effectively map the effective mapping mechanism of multi-layer neural networks. For a cloud algorithm on a cloud computing cluster and a serial algorithm on a single processor, the time required to implement the algorithm is theoretically derived, and the cloud algorithm and performance parameters (acceleration ratio) on the cloud cluster are evaluated, the optimal number and minimum number of data nodes). Finally, the experimental results show that compared with the existing algorithms, the cloud algorithm proposed in this paper has better acceleration speed, faster convergence speed and fewer iterations.

本文对多媒体通信大数据处理的技术方面进行了深入研究。首先，提出了一种基于Map-Reduce的径向基函数神经网络云计算方法。其次，为了满足多媒体通信中大数据处理的需求，训练了基于map-reduce的错误反向传播算法，以有效地映射多层神经网络的有效映射机制。对于云计算集群上的云算法和单处理器上的串行算法，理论上推导出了实现该算法所需的时间，并评估了云算法和云集群上的性能参数（加速比），得出了最佳数量和最小数量的数据节点）。最后，