## 1 Introduction
In order to reduce the complexity of the adaptive equalization algorithm in the long-distance LMF communication system, the frequency-domain equalization minimum average method based on variable step size was used to realize the mode decomposition multiplexing of multiple input and multiple output equalizers. After that, the equalization weight coefficient was modified by the least mean square adaptive algorithm of the frequency domain block, and the step factor was adjusted by the variable step function so that it could take into account the convergence speed and performance, and further use the fast Fourier transform to reduce the computational complexity. The simulation results showed that in the 112 g bit/s 1000 km small mode fiber channel communication system, the method could improve the signal Q2 factor by 3.7 dB and can be used in the 100 km small mode fiber communication system in the programmable field gate array under the same convergence. It can realize the signal demultiplexing in the system of modular division multiplexing, and realize the purpose of fast convergence and low steady-state maladjustment. In reference [3], the 66 mode division multiplexing (MDM) system was demultiplexed by the unconstrained frequency domain equalization (FDE) of multiple input and multiple output, so as to eliminate the effects of mode coupling and differential mode delay on the signal. At the same time, unconstrained least mean square (fd-lms) algorithm and unconstrained frequency domain constant modulus algorithm (fd-cma) were used to demultiplex the MDM system, so as to prove the unconstrained FDE demultiplexing effect. Then, the performance of both unconstrained fd-lms and fd-cma were compared, and the results showed that unconstrained FDE demultiplexing performance and constraining of the FDE performance is similar, but the calculation method is simple. In the mobile communication network, it is necessary to optimize the clustering processing of the text big data of the numerical attribute of the network communication big data, combine the clustering attribute features of the data for fusion scheduling and classification recognition, improve the accurate positioning of the numerical attribute of the communication big data, improve the quantitative analysis ability of the numerical attribute of the communication big data, and study the clustering method of the characteristic information of the numerical attribute of the numerical attribute of the communication [4; 5; 6]. It has great significance in improving the information recommendation ability and big data information processing ability of mobile communication networks [7; 8; 9]. The multi-dimensional text information clustering processing of communication big data numerical attributes is based on the multi-dimensional feature extraction and association rule mining of the data, combined with the sensing data acquisition method to extract the association rule feature quantity of the communication big data numerical attribute multi-dimensional text information, as well as realizing of the multi-dimensional text data classification and recognition. In this paper, a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to program and process the numerical attributes of communication big data. The mutual information features of big data numerical attributes were extracted, and the data classification was carried out by using cloud computing and adaptive quantitative recurrent classifiers. Finally, the simulation experiments were carried out to show the superior performance of this method in improving the clustering ability of numerical attribute feature information of communication big data [10; 11; 12].