Decompilation can convert binary programs into clear C-style pseudocode, which is of great value in a wide range of security applications. Existing research primarily focuses on recovering symbolic information in pseudocode, such as function names, variable names, and data types, but neglecting structural information. We observe that even when symbolic information is fully preserved, severe and complex structure distortions remain in the pseudocode, greatly impairing code readability and comprehension. In this work, we first systematically investigate structure distortions in decompiled pseudocode, revealing their variation patterns through quantitative analysis. Using open coding, we derive a taxonomy comprising six top-level categories of structure distortions. Building upon this taxonomy, we propose PseudoFix, a novel framework that combines large language models (LLMs) with retrieval-based in-context learning. PseudoFix employs semantic retrieval to select the most relevant few-shot examples that provide structure distortion knowledge, and combines this with the well-structured coding patterns learned by LLMs from vast source code repositories, to efficiently refactor distorted pseudocode. Comprehensive evaluations demonstrate that PseudoFix significantly improves pseudocode readability, achieving up to a 34% reduction in Halstead Complexity Effort and a 105% increase in BLEU-4 score. Notably, it significantly outperforms state-of-the-art approaches in both temporary variable elimination and goto statement removal tasks. Additionally, human evaluations yield consistently positive feedback from users across readability, consistency, and reasonability.