The purpose of this paper is to solve the shortcomings of traditional big data mining and analysis (BDMA); this paper compares and analyzes the denoise effect of wavelet transform and wavelet packet analysis and chooses to use wavelet packet analysis method to denoise the signal and extract fault feature vector. Finally, the extracted feature vectors are divided into two libraries, including training and testing. The fuzzy convolutional neural network (FCNN) is trained by using the training sample database data, the network weights are constantly modified, and the projection of nonlinear is computed between the input features and output features. After the expected recognition accuracy is achieved, the performance of FCNN is evaluated by using the detection samples. The performance comparison and analysis are conducted with the traditional BDMA algorithm, mainly including the comparison of recognition accuracy and learning convergence speed. Two stages of feature extraction and data mining (DM) are constructed on the big data set. The experimental results prove the effectiveness of wavelet transform feature and FCNN in analyzing and mining in big data. Finally, through the example of BDMA, this paper verifies that the organic combination of wavelet transform feature extraction and FCNN algorithm obviously improves the efficiency, meets the requirements of big data analysis, and provides potential application value for BDMA.

本文的目的是解决传统大数据挖掘和分析（BDMA）的缺点；本文对小波变换和小波包分析的去噪效果进行了比较分析，选择使用小波包分析的方法对信号进行去噪，提取故障特征向量。最后，将提取的特征向量分为两个库，包括训练和测试。使用训练样本数据库数据训练模糊卷积神经网络（FCNN），不断修改网络权重，并计算输入特征和输出特征之间的非线性投影。在达到预期的识别精度后，使用检测样本评估FCNN的性能。用传统的BDMA算法进行性能比较和分析，主要包括识别精度和学习收敛速度的比较。在大数据集上构建了特征提取和数据挖掘（DM）的两个阶段。实验结果证明了小波变换特征和FCNN在大数据分析和挖掘中的有效性。最后，以BDMA为例，验证了小波变换特征提取与FCNN算法的有机结合明显提高了效率，满足了大数据分析的要求，并为BDMA提供了潜在的应用价值。实验结果证明了小波变换特征和FCNN在大数据分析和挖掘中的有效性。最后，以BDMA为例，验证了小波变换特征提取与FCNN算法的有机结合明显提高了效率，满足了大数据分析的要求，并为BDMA提供了潜在的应用价值。实验结果证明了小波变换特征和FCNN在大数据分析和挖掘中的有效性。最后，以BDMA为例，验证了小波变换特征提取与FCNN算法的有机结合明显提高了效率，满足了大数据分析的要求，并为BDMA提供了潜在的应用价值。