Analog circuit sizing involves high-dimensional design spaces, costly simulations, and limited samples, which often reduce Bayesian optimization efficiency. A Bayesian optimization algorithm with dynamically fused acquisition functions is proposed. Latin hypercube sampling is used for initialization, while truncated Gaussian subspace sampling generates candidate points. The search center, step size, and covariance structure are updated from previous evaluations. For local Gaussian-process modeling, hyperparameter priors are introduced to form a maximum a posteriori objective, solved by multi-start L-BFGS to reduce sensitivity to initialization and local optima. During candidate selection, sigmoid weights dynamically combine upper confidence bound and expected improvement ac-cording to simulation progress, allowing the search to shift gradually from global exploration to local exploitation. Tests on three practical analog circuits show that, compared with tSS-BO, the proposed algorithm obtains up to 1.46 times as many feasible designs and improves the optimization objective by up to 19.95%, with efficient and stable convergence under limited simulation budgets.