This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.