Balancing treatment allocation over influential covariates is an important issue in clinical trials. In literature, a lot of covariate-adaptive randomization (CAR) procedures are proposed for balancing covariates. However, most studies have focused on balancing of discrete covariates. Applications of CAR for balancing continuous covariates remain comparatively rare. In this talk, we consider a general framework of CAR procedures which can balance general covariate features, such as quadratic and interaction terms which can be discrete, continuous and mixing. We show that the proposed procedures have superior balancing properties; in particular, the convergence rate of imbalance vectors can attain the best rate $O_P(1)$ for discrete covariates, continuous covariates or combinations of both discrete and continuous covariates, and at the same time, the convergence rate of the imbalance of unobserved covariates is $O_P(\sqrt n)$, where $n$ is the sample size. As an application, the asymptotic properties of the test for the treatment effects are established. The talk is based on works of Hu, Ye and Zhang (2022), Ma, Li, Zhang and Hu (2022), Zhang (2023) .