Rolling bearing health assessment relies on the constructed degradation indicators, In order to improve the monotonicity and trend of the indicators, a fusion indicator construction method considering feature burr removal is proposed. For the feature burrs appearing in the rolling bearing degradation performance characterization features that deviate from the expected degradation trend, a criterion-based adaptive burr removal strategy is used to detect and remove the burrs existing in the characterization features to improve the performance of the degradation indicators; then, principal component analysis (PCA) is used to fuse six kinds of time domain features, to remove the redundant information in the original state feature space, to maintain the global structure of the bearing degradation data, and further use the Exponentially Weighted Moving Average (EWMA) algorithm to smooth the fusion indicators to obtain high-quality degradation indicators. The experimental results verify the superiority of the proposed method in terms of monotonicity and trend.