While large language models (LLMs) have made significant progress in expanding their context windows, they still face great challenges in effectively organizing and utilizing long-term memory to maintain conversation consistency and coherence. Summarizing historical conversations has achieved remarkable performance, which, however, loses conversational trajectory and association, making it difficult to precisely combine memories from different sessions in response to current queries. To address this, we propose the Dynamic Memory Forest (DMF), a novel Consolidation-then-Growth framework for long-term open-domain conversation, which simulates the consolidation and growth processes of human memory by dynamically organizing long-term conversation histories into a memory forest of memory trees. To be specific, inspired by the principles of synaptic consolidation and plasticity from Cognitive Science, we first consolidate each session into memory units that preserve thematic coherence ("Consolidation"). Then, we first structure these units into memory trees and then grow the forest by dynamically connecting them through an evolutionary grafting mechanism, called Group Relative Voting Optimization, which mimics synaptic connection to decide whether a new memory tree should be grafted onto the existing forest or grow independently ("Growth"). For retrieval, we design an Entropy-Driven Memory Walk, constructing a logically coherent memory path via a navigation policy that prioritizes exploring high-entropy nodes. Experiments on three long-term conversation datasets show that our DMF significantly outperforms baselines in enhancing response generation for LLMs.