The invention discloses a
paleontology diversity analysis method based on a dynamic
machine learning joint correction model, and the method comprises the steps: obtaining a
paleontology data sample, and carrying out the missing value
processing, abnormal value detection, data
standardization and
time data mapping preprocessing of the
paleontology data sample; performing dynamic interval division on the preprocessed paleontology data sample, and realizing adaptive time interval division by adopting multiple algorithms; non-uniform
weight distribution is realized through data driving weights; enhanced
diversity analysis is carried out on an optimized
data set, including interval division
processing on the
data set, feature evidence extraction and interval weight calculation, integrated interval weighted diversity dynamic calculation, weighted survivor analysis, interval sub-sampling analysis and other methods, so that diversity calculation is more accurate; and outputting a diversity curve, a data table and a performance
evaluation result. The method better adapts to the imbalance of fossil records, and the recognition capability of key biological events is improved.