## 4 Simulation Experiment Analysis  
In order to test the performance of this method in the clustering of numerical attribute information of communication big data and verify the effectiveness and feasibility of this method, simulation experiments were carried out. On the basis of the Deep Web database and MATLAB, we empirically designed a fuzzy weighted clustering algorithm. The attribute of the big data sample was set to 6, and the initial confidence level of data fuzziness was. The clustering was 95%, the critical value was 1.24, and the judgment threshold was 0.13. The embedding dimension of the feature space distribution was set to M = 4, the data length of the test sample was set at2000 years, and the simulation time was120s. The method of this paper was used to compare study [2] and study [3] for experimental and comparative analysis. The main parameters of the experiment are shown in Table 1.  
According to the above simulation environment and parameter settings, big data fuzzy weighted clustering analysis communication big data numerical attribute Mu executed one-dimensional text, and the original data distribution is shown in Figure 7. \begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Description or Value** \\ \hline Total node value & 400 \\ Spacing between nodes & 50–100 m \\ Queue control & Optimize queue \\ Experimental wireless channel model & MICAZE \\ Experimental time & Longest 900 s \\ Experimental range & \(1000\times 1000\) m \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation experiment parameters. Figure 6: Data clustering output. Taking the data of Figure 7 as the research object, the fuzzy weighted clustering of the data was carried out, with the data classification being carried out by using cloud computing and adaptive quantitative recurrent classifiers. The clustering output results are shown in Figure 8.  
Figure 8 shows that big data fuzzy weighted clustering can be effectively realized by using this method; the accuracy of data classification was high, and the error rate was small. The performance of big data fuzzy weighted clustering was tested by different methods. The comparison results are shown in Figure 9. It can be seen from Figure 9 that the fuzzy weighted clustering error rate of big data was lower than that of the other two comparison methods after adopting this method, which proved that this method has obvious application advantages. Figure 8: Big data fuzzy weighted clustering output. Figure 7: Raw data distribution. Figure 9: Performance comparison of fuzzy weighted clustering of data. In order to test the effect of frequency domain equalization of communication security, this paper compared study [2] and study [3], and compared the performance of this method in the routing protocol that uses node location information to make decisions, finding that the performance of this method was better in the network with high dynamic topology caused by node movement. The comparison results of the average energy consumption of the three methods for successful transmission of a single packet are shown in Figure 10.  
It can be seen from Figure 10 that in the comparison of the three methods for successfully receiving the average energy consumption of a single packet, the unit energy consumption of study [2] and study [3] was more, and the text energy consumption was the least, and thus the method in this paper is the best for the communication security frequency-domain equalization control effect. When the communication channel was weak, its noise had a great influence on the capacity of the communication channel. However, when the minimum mean square error equalization was used, the optimal energy constraint and frequency-domain equalization were controlled to ensure the optimal energy in the frequency domain equalization control, and the average energy consumption control of a single data packet was successfully transmitted. The communication channel information obtained by channel estimation was equalized in the frequency domain.