Objective: To explore the potential classification of 24 hour activity time allocation among college students in Yinchuan and its association with active health levels, so as to provide references for optimizing activity time allocation to enhance active health levels. Methods: From November 18 to December 6, 2024, a stratified cluster random sampling method was used to select 2 422 first and second year college students from full time undergraduate institutions in Yinchuan. The Chinese College Students 24 hour Movement Behaviors Questionnaire (24 h MBQ) and Active Health Behavior Scale were used to assess 24 hour activity time allocation and evaluate active health levels. Latent profile analysis (LPA) was employed to categorize activity types, and a binary Logistic regression analysis was conducted to analyze the relationship between active health levels and activity types. Results: A total of 1 087 students (44.9%) were found of meeting active health standards, and significant statistical differences were found in active health levels across different genders, grades, academic qualities, sources of origin and academic categories ( χ 2= 22.03 , 7.65, 25.50, 10.12, 43.44, all P <0.01). Moreover, significant statistical differences could also be found among college students 24 hour activity time across different genders, ages, grades, sources of origin, academic qualities, and academic categories ( t/Z/H/F=-5.70-111.39, P <0.05).The 24 hour activity time allocation was classified into four types:academic high ( 6.9 %), low activity rest (8.8%), light activity (67.8%), and high activity dynamic (16.4%). Significant statistical differences were observed in activity time allocation categories across different ages, academic qualities and academic categories ( χ 2=15.52-108.46, all P <0.05). Using the high activity dynamic type as a reference, the light activity type ( OR=0.39, 95%CI =0.31-0.50), low activity rest type ( OR=0.10, 95%CI =0.06-0.15), and academic high type ( OR=0.03, 95%CI =0.02-0.07) had lower active health levels among college students (all P <0.01). Conclusion There is a significant difference in 24 hour activity time allocation among college students in Yinchuan, and different activity types are associated with active health levels.