## 1 Introduction  
With the development of the economy, the progress of science and technology, and the improvement of talent technology, the development of wireless network mobile communication technology has been greatly promoted. With the increasing demand for internet communication, the process of improving the data transmission performance of wireless network mobile communication can help people to have a more comprehensive and clear understanding of the world, to pay attention to the dynamics of the world in real time, and to understand a more abundant amount of knowledge. In the new era, people cannot be separated from wireless network mobile communication data transmission. Whether in work or in daily life, wireless network mobile communication data transmission has penetrated into every corner and has promoted people's interaction. Wireless network mobile communication data transmission depends on optical cable optical fiber. Therefore, the superior performance of optical cable optical fiber communication transmission represents the top economic development and efficiency in the world [1]. In our country, at the end of 2017, the number of optical fiber access (FTTH/0) portsreached 657 million, and the full function standardization of the first stage of the 5G network was completed in 2018. It is expected that in 2020, the optical fiber access (optical access) port will reach 657 million, with the full-function standardization of the first stage of 5G network having been completed in 2018. The experiment of the 5G mobile communication system in China has been carried out to promote the progress and development of society in the process of continuously improving the data transmission performance of mobile communication in a wireless network. It is necessary to optimize the clustering analysis of communication big data, establish the transmission fusion control model of communication big data, and combine big data mining method to continue the adaptive clustering of communication big data. Fuzzy weighted clustering is a kind of unsupervised pattern recognition problem, which gathers things into groups according to their attributes, making the similarity within the groups as large as possible and the similarity between the groups as small as possible. Traditional clustering methods include C-means clustering and FCM soft clustering. With the increasing amount of data to be clustered, the efficiency of direct clustering with traditional methods is very low. High dimensional datasets involve spatial distribution, whereas the effectiveness of traditional clustering algorithm has strong dependence on the spatial distribution of samples. For example, C-means clustering has a better clustering effect when the feature space is a hypersphere, whereas it has a worse clustering effect when the feature space is a hypersphere, and FCM soft clustering has a better clustering effect when the feature space is an ellipsoid. In order to overcome the dependence of clustering efficiency on the spatial distribution of samples, a high-dimensional data fuzzy clustering algorithm based on genetic algorithm is adopted for the subset clustering of each high-dimensional communication big dataset. The purpose is to transform the fuzzy non similarity between high-dimensional samples into Euclidean distance between two-dimensional samples, that is, to transform the difference between high-dimensional samples into the difference between two-dimensional samples, so as to realize the mapping from high-dimensional samples to two-dimensional samples. Finally, the FCM algorithm can be used to cluster the two-dimensional samples. In reference [2], it was proposed that there are mode coupling and differential mode delay in the LMF method, and thus the adaptive equalization algorithm was used as the compensation mechanism.