Abstract: This paper introduces an online scoring method for cervical vertebrae health based on multiple instance learning (MIL) of multiple-valued input,in order to assess cervical vertebrae health score and solve the data labeling difficulty.It is only necessary to simply label the long-term sequence of cervical vertebrae motion data during the training phase to estimate the health score of the cervical short-term state.Firstly,the multiple-valued input is divided into sub-classifiers of multiple binary inputs and trained separately.Then use the Gaussian model to fuse the instance scores trained by each sub-classifier.Finally,the bag score is calculated with a new scoring mechanism and the cervical vertebrae health can be assessed in real-time.Qualitative and quantitative experiments include the bag score prediction accuracy,instance visualization analysis,bag score curve analysis and real-time scoring analysis,which illustrate the effectiveness of the algorithm in assessing the health of the cervical vertebrae.