完整的python代码实现包括添加异常处理、登录微博账号、使用代理等功能以提高爬取稳定性和效率：爬取1000名微博用户信息，包括用户发表的微博内容、关注对象、粉丝等，对爬取内容清洗并提取用户特征，对1000名用户根据爬取的特征进行聚类分析，构建每个类下的用户画像(可通过词云图进行展示)，并针对近日的微博热点进行话题推荐。

以下是一个添加了异常处理、登录微博账号、使用代理的完整Python代码示例：

import requests
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from wordcloud import WordCloud
import matplotlib.pyplot as plt

# 登录微博账号
def login_weibo(username, password):
    login_url = 'https://passport.weibo.cn/sso/login'
    data = {
        'username': username,
        'password': password,
        'savestate': 1,
        'r': '',
        'ec': 0,
        'pagerefer': '',
        'entry': 'mweibo',
        'wentry': '',
        'loginfrom': '',
        'client_id': '',
        'code': '',
        'qq': '',
        'hff': '',
        'hfp': ''
    }
    session = requests.Session()
    response = session.post(login_url, data=data)
    if response.status_code == 200:
        print('登录成功！')
        return session
    else:
        print('登录失败！')
        return None

# 爬取用户信息的函数
def crawl_user_info(session, user_id):
    url = f'https://weibo.com/u/{user_id}'
    
    try:
        response = session.get(url)
        response.raise_for_status()
    except requests.RequestException as e:
        print(f'爬取用户{user_id}信息失败：{str(e)}')
        return None
    
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # 解析用户发表的微博内容、关注对象、粉丝等信息
    weibo_content = [weibo.text for weibo in soup.select('.WB_text')]
    followings = [following.text for following in soup.select('.follow_item_name')]
    followers = [follower.text for follower in soup.select('.follow_item_name')]
    
    return {
        'user_id': user_id,
        'weibo_content': weibo_content,
        'followings': followings,
        'followers': followers
    }

# 使用代理进行爬取
def crawl_with_proxy(session, user_id, proxy):
    proxies = {
        'http': proxy,
        'https': proxy
    }
    session.proxies.update(proxies)
    
    user_info = crawl_user_info(session, user_id)
    
    # 清除代理设置
    session.proxies = {}
    
    return user_info

# 爬取1000名用户信息
def crawl_user_profiles(username, password):
    session = login_weibo(username, password)
    if session is None:
        return []
    
    user_info_list = []
    proxy_list = ['proxy1', 'proxy2', 'proxy3']  # 假设这是代理列表
    
    for user_id in range(1, 1001):
        try:
            user_info = crawl_with_proxy(session, user_id, proxy_list[user_id % len(proxy_list)])
            if user_info is not None:
                user_info_list.append(user_info)
        except Exception as e:
            print(f'爬取用户{user_id}信息失败：{str(e)}')
    
    return user_info_list

# 清洗并提取用户特征
def clean_and_extract_features(user_info_list):
    corpus = []
    
    for user_info in user_info_list:
        weibo_content = ' '.join(user_info['weibo_content'])
        corpus.append(weibo_content)
    
    vectorizer = TfidfVectorizer()
    X = vectorizer.fit_transform(corpus)
    
    return X

# 聚类分析
def perform_clustering(X, n_clusters):
    kmeans = KMeans(n_clusters=n_clusters)
    kmeans.fit(X)
    
    return kmeans

# 构建用户画像
def build_user_profiles(user_info_list, kmeans, n_clusters):
    user_profiles = [[] for _ in range(n_clusters)]
    
    for i, user_info in enumerate(user_info_list):
        cluster_label = kmeans.labels_[i]
        user_profiles[cluster_label].append(user_info)
    
    return user_profiles

# 生成词云图
def generate_wordclouds(user_profiles):
    for i, user_profile in enumerate(user_profiles):
        text = ' '.join([weibo_content for user_info in user_profile for weibo_content in user_info['weibo_content']])
        wordcloud = WordCloud(background_color='white').generate(text)
        plt.figure()
        plt.imshow(wordcloud, interpolation='bilinear')
        plt.axis('off')
        plt.title(f'Cluster {i+1} User Profile')
        plt.show()

# 进行话题推荐
def recommend_topics(user_profiles, hot_topics):
    for i, user_profile in enumerate(user_profiles):
        topic_scores = []
        
        for user_info in user_profile:
            weibo_content = ' '.join(user_info['weibo_content'])
            score = sum(weibo_content.count(topic) for topic in hot_topics)
            topic_scores.append(score)
        
        top_user_indices = sorted(range(len(topic_scores)), key=lambda k: topic_scores[k], reverse=True)[:5]
        top_users = [user_profile[index] for index in top_user_indices]
        
        print(f'Top users in Cluster {i+1}:')
        
        for user_info in top_users:
            print(f"User ID: {user_info['user_id']}, Weibo Content: {user_info['weibo_content'][:10]}")

# 主函数
def main():
    # 用户名和密码
    username = 'your_username'
    password = 'your_password'
    
    # 热门话题
    hot_topics = ['topic1', 'topic2', 'topic3']  # 假设这是近日的微博热点话题
    
    # 爬取用户信息
    user_info_list = crawl_user_profiles(username, password)
    
    # 清洗并提取用户特征
    X = clean_and_extract_features(user_info_list)
    
    # 聚类分析
    n_clusters = 5  # 设置聚类簇数
    kmeans = perform_clustering(X, n_clusters)
    
    # 构建用户画像
    user_profiles = build_user_profiles(user_info_list, kmeans, n_clusters)
    
    # 生成词云图
    generate_wordclouds(user_profiles)
    
    # 进行话题推荐
    recommend_topics(user_profiles, hot_topics)

# 执行主函数
if __name__ == '__main__':
    main()

注意：在代码中的以下部分，需要替换为你自己的微博账号信息：

# 用户名和密码
username = 'your_username'
password = 'your_password'

此外，你还需要将代理列表和热门话题根据需求进行替换。