Early intervention in children's mental health is critical, and large language models (LLMs) offer effective psychological companionship support. However, existing psychological LLMs still face significant limitations in addressing children's unique needs, particularly in language cognition and companionship strategies. To overcome these issues, we introduce PsyChild, the first framework specifically designed as an AI expert for psychological companionship in children, featuring the dataset PsyCINS, the LLM PsyCGPT, and the benchmark PsyCBEN. To overcome the constraints of data privacy and crowdsourced annotations, we innovatively construct PsyCINS, a 25.4k multi-turn dataset of child psychology dialogues, using two LLM-guided methods: (1) Multi-channel children's daily conversations grounded in psychological theory. (2) Single-turn dialogues from professional psychological counseling platform. Unlike most methods that directly generate dialogues based on LLMs, our PsyCINS achieves superior public acceptance and language style in comprehensive evaluations. Leveraging PsyCINS, we fine-tune the Qwen2.5-7B-Instruct base LLM to develop the psychological Q\&A LLM PsyCGPT. Moreover, we introduce PsyCBEN, the first benchmark with 583 dialogues powered by the advanced, human-like GPT-4.1 as the judge, to assess the fine-grained abilities of LLMs in children's psychological dialogues. Evaluations across 19 mainstream LLMs reveal significant differences between general-purpose and psychological LLMs, with our PsyCGPT excelling. PsyChild framework demonstrates exceptional effectiveness and superiority in the field of children's mental health. More details are available at: https://anonymous.4open.science/r/PsyChild.