Based on extended negative binomial thinning operator, an integer-valued INAR(1) model is constructed by prespecifying the distribution for the innovation process. The probabilistic properties of the model are given and the parameters of the model are estimated by using the quasi likelihood estimation method, and least squares and maximum likelihood estimation methods are also considered. The validity of these estimation methods is evaluated by numerical simulation, and the application of the model is given based on the actual data. Through comparison, it is concluded that the INAR (1) based on the extended negative binomial thinning operator with the innovation process of geometric distribution is a more suitable model for the data.