A number of authors have focused on this study to examine how huge data are perceived. A novel big data classification paradigm is introduced by the work’s preprocessing, feature extraction and classification techniques. Data normalization is carried out at the preprocessing stage. The MapReduce framework is then utilized to manage the massive data. Statistical features (mean, median, min/max and SD), higher-order statistical features (skewness, kurtosis and enhanced entropy), and correlation-based features are all extracted prior to classification. The Bi-LSTM and deep maxout hybrid classification model classifies the data during the reduction stage. To assure classification accuracy, training will also be deployed by the new Hybrid Butterfly Positioned Coot Optimization (HBPCO) algorithm. The proposed method’s accuracy of 97.45% beats the methods of NN (85.13%), CNN (83.78%), RNN (78.37%), Bi-LSTM (82.43%) and SVM (87.83%).

许多作者都专注于这项研究，以检验人们如何感知海量数据。该工作的预处理、特征提取和分类技术引入了一种新颖的大数据分类范式。数据标准化是在预处理阶段进行的。然后利用MapReduce框架来管理海量数据。统计特征（均值、中值、最小/最大和 SD）、高阶统计特征（偏度、峰度和增强熵）以及基于相关性的特征均在分类之前提取。Bi-LSTM 和深度 maxout 混合分类模型在缩减阶段对数据进行分类。为了确保分类准确性，还将通过新的混合蝴蝶定位优化 (HBPCO) 算法来部署训练。该方法的准确率为 97.45%，优于 NN (85.13%)、CNN (83.78%)、RNN (78.37%)、Bi-LSTM (82.43%) 和 SVM (87.83%) 的方法。