Smart Chinese Analysis插件将Lucene的Smart Chinese分析模块集成到Elasticsearch中，用于分析中文或中英文混合文本。 支持的分析器在大型训练语料库上使用基于隐马尔可夫（Markov）模型的概率知识来查找简体中文文本的最佳分词。 它使用的策略是首先将输入文本分解为句子，然后对句子进行切分以获得单词。 该插件提供了一个称为smartcn分析器的分析器，以及一个称为smartcn_tokenizer的标记器。 请注意，两者均不能使用任何参数进行配置。
要将smartcn Analysis插件安装在Elasticsearch Docker容器中，请使用以下屏幕截图中显示的命令。 然后，我们重新启动容器以使插件生效：
./bin/elasticsearch-plugin install analysis-smartcn
在Elasticsearch的安装目录运行上面的命令。显示的结果如下：
$ ./bin/elasticsearch-plugin install analysis-smartcn
-> Downloading analysis-smartcn from elastic
[=================================================] 100%
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.bouncycastle.jcajce.provider.drbg.DRBG (file:/Users/liuxg/elastic/elasticsearch-7.3.0/lib/tools/plugin-cli/bcprov-jdk15on-1.61.jar) to constructor sun.security.provider.Sun()
WARNING: Please consider reporting this to the maintainers of org.bouncycastle.jcajce.provider.drbg.DRBG
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
-> Installed analysis-smartcn
(base) localhost:elasticsearch-7.3.0 liuxg$ ./bin/elasticsearch-plugin list
analysis-icu
analysis-ik
analysis-smartcn
pinyin
上面显示我们已经成功地把analysis-smartcn安装成功了。针对docker的安装，我们可以通过如下的命令来进入到docker里，再进行安装：
$ docker exec -it es01 /bin/bash
[root@ec4d19f59a7d elasticsearch]# ls
LICENSE.txt README.textile config jdk logs plugins
NOTICE.txt bin data lib modules
[root@ec4d19f59a7d elasticsearch]#
在这里es01是docker中的Elasticsearch实例。具体安装请参阅我的文章“Elastic：用Docker部署Elastic栈”。
注意：在我们安装好smartcn分析器后，我们必须重新启动Elasticsearch使它开始起作用。
实例
在下面，我们在Kibana中用一个实例来展示这个用法：
POST _analyze
{
"text": "股市，投资，稳，赚，不，赔，必修课，如何，做，好，仓，位，管理，和，情绪，管理",
"analyzer": "smartcn"
}
显示结果：
{
"tokens" : [
{
"token" : "股市",
"start_offset" : 0,
"end_offset" : 2,
"type" : "word",
"position" : 0
},
{
"token" : "投资",
"start_offset" : 3,
"end_offset" : 5,
"type" : "word",
"position" : 2
},
{
"token" : "稳",
"start_offset" : 6,
"end_offset" : 7,
"type" : "word",
"position" : 4
},
{
"token" : "赚",
"start_offset" : 8,
"end_offset" : 9,
"type" : "word",
"position" : 6
},
{
"token" : "不",
"start_offset" : 10,
"end_offset" : 11,
"type" : "word",
"position" : 8
},
{
"token" : "赔",
"start_offset" : 12,
"end_offset" : 13,
"type" : "word",
"position" : 10
},
{
"token" : "必修课",
"start_offset" : 14,
"end_offset" : 17,
"type" : "word",
"position" : 12
},
{
"token" : "如何",
"start_offset" : 18,
"end_offset" : 20,
"type" : "word",
"position" : 14
},
{
"token" : "做",
"start_offset" : 21,
"end_offset" : 22,
"type" : "word",
"position" : 16
},
{
"token" : "好",
"start_offset" : 23,
"end_offset" : 24,
"type" : "word",
"position" : 18
},
{
"token" : "仓",
"start_offset" : 25,
"end_offset" : 26,
"type" : "word",
"position" : 20
},
{
"token" : "位",
"start_offset" : 27,
"end_offset" : 28,
"type" : "word",
"position" : 22
},
{
"token" : "管理",
"start_offset" : 29,
"end_offset" : 31,
"type" : "word",
"position" : 24
},
{
"token" : "和",
"start_offset" : 32,
"end_offset" : 33,
"type" : "word",
"position" : 26
},
{
"token" : "情绪",
"start_offset" : 34,
"end_offset" : 36,
"type" : "word",
"position" : 28
},
{
"token" : "管理",
"start_offset" : 37,
"end_offset" : 39,
"type" : "word",
"position" : 30
}
]
}