Abstract: Due to the complexity of digital information, massive and multi-angle unstructured big data, and external interference, data structure damage and other factors cause its information loss, a missing value interpolation algorithm for unstructured big data based on transfer learning is proposed. Through the migration learning algorithm, the missing parts of unstructured big data are predicted, and the naive Bayesian algorithm is used to classify data features, to measure the weight value between attributes, to clarify the feature difference vector of data categories, and to identify the degree of feature difference. The kernel regression model is used to implement nonlinear mapping for the missing part of the data, and the polynomial change coding is used to describe the cross-space complementary condition of the data, completing the interpolation of the missing value of unstructured big data. The experimental results show that the proposed algorithm can effectively complete the interpolation of missing values of unstructured large data, has good interpolation effect and can improve the interpolation accuracy.

Key words: transfer learning, unstructured big data, imputation of missing values, missing value prediction, kernel regression function