In this paper, we study the convergence rates of empirical spectral distributions of large dimensional quaternion sample covariance matrices. Assume that the entries of Xn (p×n) are independent quaternion random variables with means zero, variances 1 and uniformly bounded sixth moments. Denote Sn=1nXnXn∗. Using Bai’s inequality, we prove that the expected empirical spectral distribution (ESD) converges to the limiting Marčenko–Pastur distribution with the ratio of dimension to sample size yp=p/n at a rate of O(n−1/2an−3/4) when an>n−2/5 or O(n−1/5) when an≤n−2/5, where an=(1−yp)2. Moreover, the rates for both the convergence in probability and the almost sure convergence are also established. The weak convergence rate of the ESD is O(n−2/5an−1/2) when an>n−2/5 or O(n−1/5) when an≤n−2/5. The strong convergence rate of the ESD is O(n−2/5+ηan−1/2) when an>κn−2/5 or O(n−1/5) when an≤κn−2/5 for any η>0 where κ is a positive constant.