The invention provides a method for establishing a student growth portrait based on group sparse fusion hospital
big data, and the method comprises: obtaining multi-source high-dimensional data of students, including student basic
information data, school
learning data, daily life data and hospital regular training practice data; performing data preprocessing on the multi-source high-dimensional data; compressing and storing the preprocessed sample
data set based on a triple representation method in a sparse
algorithm; grouping the feature tags with strong correlation by using a binary K-meansclustering
algorithm, and constructing a student growth portrait
tag system which is more suitable for actual conditions of students; combining the labels into different feature
label groups, and acquiring clustering distribution information of the sample data feature
label groups based on
local learning; and under the guidance of clustering distribution information, obtaining feature weights bymeans of group sparse regression, wherein the feature weights are used for evaluating the importance of features, and selecting corresponding important feature tags. A student growth portrait
label system is established according to the high-dimensional data, and a student growth portrait fused with hospital
big data stereoscopicity is constructed. By implementing the method, the collected data can be more complete, the constructed student portrait
index system is more stereoscopic, complete and accurate, and subjective randomness is avoided to a certain extent.