The stochastic Newton recursive algorithm is studied for dual-rate system identification. Owing to a lack of intersample measurements, the single-rate model cannot be identified directly. The auxiliary model technique is adopted to provide the intersample estimations to guarantee the recursion process continues. Intersample estimations have a great influence on the convergence of parameter estimations, and one-step innovation may lead to a large fluctuation or even divergence during the recursion. In the meantime, the sample covariance matrix may appear singular. The recursive process would cease for these reasons. In order to guarantee the recursion process and to also improve estimation accuracy, multi-innovation is utilized for correcting the parameter estimations. Combining the auxiliary model and multi-innovation theory, the auxiliary-model-based multi-innovation stochastic Newton recursive algorithm is proposed for time-invariant dual-rate systems. The consistency of this algorithm is analyzed in detail. The final simulations confirm the effectiveness of the proposed algorithm.