python属性加权聚合相似度算法，通过设置阈值实现中文实体对齐自动化，将合并阈值设置为 0.8，独立阈值设置为 0.5将两个实体进行对比的详细代码

以下是一个示例代码，用于实现中文实体对齐的属性加权聚合相似度算法，并通过设置阈值进行自动化对比。

import numpy as np
from collections import Counter
import jieba

# 计算两个字符串的相似度
def string_similarity(str1, str2):
    str1 = set(jieba.lcut(str1))
    str2 = set(jieba.lcut(str2))
    intersection = len(str1 & str2)
    union = len(str1 | str2)
    return intersection / union

# 计算属性相似度
def attribute_similarity(attr1, attr2):
    if isinstance(attr1, str) and isinstance(attr2, str):
        return string_similarity(attr1, attr2)
    elif isinstance(attr1, list) and isinstance(attr2, list):
        counter1 = Counter(attr1)
        counter2 = Counter(attr2)
        intersection = sum((counter1 & counter2).values())
        union = sum((counter1 | counter2).values())
        return intersection / union
    else:
        return 0

# 计算实体相似度
def entity_similarity(entity1, entity2, weights):
    total_similarity = 0
    for attr1, attr2, weight in zip(entity1, entity2, weights):
        attr_similarity = attribute_similarity(attr1, attr2)
        total_similarity += attr_similarity * weight
    return total_similarity

# 对比两个实体并进行自动化实体对齐
def align_entities(entities1, entities2, merge_threshold, independent_threshold):
    num_entities1 = len(entities1)
    num_entities2 = len(entities2)
    merge_indices = []
    independent_indices = []

    for i in range(num_entities1):
        for j in range(num_entities2):
            similarity = entity_similarity(entities1[i], entities2[j], weights=[1, 1, 0.5])
            if similarity >= merge_threshold:
                merge_indices.append((i, j))
            elif similarity >= independent_threshold:
                independent_indices.append((i, j))

    return merge_indices, independent_indices

# 示例数据
entities1 = [
    ["张三", "30", ["男性", "工程师"]],
    ["李四", "25", ["女性", "医生"]],
    ["王五", "35", ["男性", "教师"]]
]

entities2 = [
    ["张三", "31", ["男性", "工程师"]],
    ["李四", "26", ["女性", "医师"]],
    ["赵六", "35", ["女性", "教师"]]
]

# 实体对比并进行自动化实体对齐
merge_threshold = 0.8
independent_threshold = 0.5
merge_indices, independent_indices = align_entities(entities1, entities2, merge_threshold, independent_threshold)

print("合并实体对比结果:")
for i, j in merge_indices:
    print(f"实体1: {entities1[i]}, 实体2: {entities2[j]}")

print("\n独立实体对比结果:")
for i, j in independent_indices:
    print(f"实体1: {entities1[i]}, 实体2: {entities2[j]}")

在上面的代码中，使用了中文分词工具jieba对字符串进行分词，然后计算分词后的字符串的相似度。其余部分与之前的示例代码相似。

请注意，这只是一个简单的示例代码，实际应用中可能需要根据具体情况进行调整和改进。