An article takes you to use the idea of modeling to solve the problem of Teddy Cup-smart government affairs (classification of public messages with code)

1. Problem analysis

When processing the masses' messages on the online politics platform, the staff first classifies the messages according to a certain classification system (refer to the three-level labeling system for content classification provided in Annex 1), so that the masses' messages can be subsequently assigned to the corresponding functional departments for processing. At present, most e-government systems still rely on manual processing based on experience, which has problems such as large workload, low efficiency, and high error rate. Please build a first-level label automatic intelligent classification model of the message content based on the data given in Annex 2.

Analyzing the topic shows that the task belongs to the text classification task in natural language processing. Attachment 2 contains 9,210 messages posted by the masses on the online platform, which are divided into 7 categories: urban and rural construction, environmental protection, transportation, education and culture, labor and social security, business and tourism, and health and family planning. Each