最后，我通过下面的代码找到了解决方案，主要问题是：
1.我没有正确地指定.json文件的层，因此完全加载它会消耗太多的内存。
1.由于上述原因，我使代码适用于一个产品类别的时间。这种方式工作。
因此，我可以通过指定从.json文件中提取的正确层来解决这个问题，而不是提取整个文件并在以后进行过滤。此外，我修改了代码，一次只处理一个产品类别，以改善内存使用。

import pandas as pd
import json
import os

# Function to load a JSON file into a Pandas DataFrame
def load_json_to_dataframe(json_file_path):
    with open(json_file_path, 'r') as json_file:
        # Load JSON file into a Python dictionary
        doc = json.load(json_file)
        # Extract the creation time of the file
        file_creation_time = os.path.getctime(json_file_path)
        # Convert the creation time to a datetime object
        file_creation_time = pd.to_datetime(file_creation_time, unit='s')
        # Normalize the JSON data and add the creation time as a new column
        df = pd.json_normalize(doc, meta=['id', 'title', 'condition', 'permalink',
                                          'category_id', 'domain_id', 'thumbnail',
                                          'currency_id', 'price', 'sold_quantity',
                                          'available_quantity', ['seller', 'id'],
                                          ['seller', 'nickname'], ['seller', 'permalink'],
                                          ['address', 'state_name'], ['address', 'city_name']])
        # Reorder the columns to have the file_creation_time column after the id column
        df = df[['id', 'title', 'condition', 'permalink', 'category_id', 'domain_id',
                 'thumbnail', 'currency_id', 'price', 'sold_quantity', 'available_quantity',
                 'seller.id', 'seller.nickname', 'seller.permalink', 'address.state_name',
                 'address.city_name']]
        # Add the file creation time column to the DataFrame
        df['file_creation_time'] = file_creation_time
        return df

# Function to read multiple JSON files into a single Pandas DataFrame
def read_json_files(folder_path, categories=None, batch_size=1000):
    if categories is None:
        # If no categories are specified, read all files that end in '.json'
        json_files = [f for f in os.listdir(folder_path) if f.endswith('.json')]
    else:
        # If categories are specified, read only files that correspond to those categories
        json_files = [f for f in os.listdir(folder_path) if f.endswith('.json') and any(category in f for category in categories)]
    # Split the list of files into batches of a given size
    batches = [json_files[i:i+batch_size] for i in range(0, len(json_files), batch_size)]
    # Read each batch of files into a list of DataFrames
    dfs = []
    for batch in batches:
        batch_dfs = [load_json_to_dataframe(os.path.join(folder_path, f)) for f in batch]
        dfs.append(pd.concat(batch_dfs, ignore_index=True))
    # Concatenate all DataFrames into a single DataFrame
    return pd.concat(dfs, ignore_index=True)

# Specify the categories of files to read and the folder path
categories = ['MLC4922.json']
folder_path = 'C:/path/to/folder/files'

# Read the JSON files into a single DataFrame
combined_dataframe = read_json_files(folder_path, categories)