Abstract: 【Purpose/significance】Due to the high dimension and imbalance of multi label text in self-media platform,the text classifi-
cation effect is poor.Therefore,it is of great significance to study the multi label text classification method of University Library's self-media platform in 5G environment【. Method/process】Firstly,this paper preprocesses the multi label text of University Library's self-media platform in 5G environment,including meaningless data removal,text segmentation,and stop words removal; Then use the im-
proved principal component analysis method for multi-label text dimensionality reduction,and use the vector space model to achieve
text balance processing; Finally, based on the processed text, a text classifier is constructed using Adaboost and SVM algorithms to
achieve multi-label text classification.【Result/conclusion】The experimental results show that the self-media platform label text clas-
sification method proposed in this paper can reduce the Hamming loss,increase the F1 value,and have a good multi-label text classifi-
cation effect, and it is less time-consuming and reliable.【Innovation/limitation】Due to the insufficient number of data sets in this
study,the results obtained in terms of testing and verification have certain limitations. Therefore,in future research,we expect to use a
richer database to make further improvements and innovations to the designed method.